Txt/Hyp term similarities

The rows are the txt words. The columns are hyp words.
Resource summary:
Acronym: AcronymLexicalResource
BasicWN: null
Country: CountryLexicalResource
Cyc: null
DekangLin: DekangLinLexicalResource
Google: null
InfoMap: InfoMapLexicalResource
Morpho: MorphoLexicalResource
NomBank: NomBankLexicalResource
Number: NumberLexicalResource
Ordinal: OrdinalLexicalResource
Preposition: PrepositionLexicalResource
Ravichandran: RavichandranLexicalResource
LeacockChodorowWN: null
JiangConrathWN: JiangConrathWNLexicalResource
ResnikWN: null
LinWN: LinWNLexicalResource
HirstWN: null
AdaptedLeskWN: null
EdgeWN: null
StringSim: StringSimLexicalResource
WebLexicalEntailment: null
CorpusLexicalEntailment: null
WordNet: null



Inference ID: 2161

Txt: City officials fired the captain of the crashed Staten Island ferry.

Hyp: Staten Island ferry captain has refused to talk with investigators. (don't know)

Staten_Island
NNP
ferry
NN
captain
NN
has
VBZ
refused
VBN
to
TO
talk
VB
investigators
NNS
City:NNP 10.50   8.27   8.39 15.00 15.00 20.00 15.00   7.15
officials:NNS 10.50   8.28   5.13 15.00 10.87 20.00 13.61   4.81
fired:VBD 15.50 13.37 11.19 10.00   6.08 20.00   9.59 10.73
the:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00
captain:NN 10.50   9.24   0.00 15.00 12.77 20.00 15.00   7.12
the:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00
crashed:NNP 10.50   5.34   9.42 15.00 15.00 20.00 15.00   5.79
Staten_Island:NNP   0.00 10.50 10.50 15.50 15.50 20.50 15.50 10.50
ferry:NN 10.50   0.00   9.24 15.00 14.08 20.00 15.00   8.71
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.12 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added investigators[investigators-NNS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "crashed" of "ferry" dropped on aligned hyp word "ferry"
-1.00  1.00 NullPunisher.other : talk
-1.00  1.00 NullPunisher.other : refused
-1.00  1.00 NullPunisher.other : investigators
-0.05  1.00 NullPunisher.aux : has
-0.10  1.00 NullPunisher.functionWord : to
-2.00  1.00 RootEntailment.unalignedRoot : "refused" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.3101
Threshold: -1.8794


Inference ID: 702

Txt: Budapest again became the focus of national political drama in the late 1980s, when Hungary led the reform movement in eastern Europe that broke the communist monopoly on political power and ushered in the possibility of multiparty politics.

Hyp: In the late 1980s Budapest became the center of the reform movement. (yes)

the
DT
late
JJ
1980s
NNS
Budapest
NNP
became
VBD
the
DT
center
NN
the
DT
reform_movement
NN
Budapest:NNP 20.50 12.50 10.17   0.50 15.50 20.50   4.46 20.50   9.85
again:RB 20.00 12.50 15.50 15.00 20.00 20.00 15.00 20.00 15.00
became:VBD 20.00 12.50 15.50 15.00   0.00 20.00 15.00 20.00 15.00
the:DT   0.00 20.50 20.50 20.00 20.00   0.00 20.00   0.00 20.00
focus:NN 20.00 12.50   9.30   9.46 15.00 20.00   7.91 20.00   8.77
national:JJ 20.00 10.50 12.10 12.00 12.00 20.00   8.82 20.00 12.00
political:JJ 20.00 10.50 10.40 12.00 12.00 20.00 10.74 20.00 12.00
drama:NN 20.00 10.68   9.39   9.53 15.00 20.00   8.07 20.00   8.89
the:DT   0.00 20.50 20.50 20.00 20.00   0.00 20.00   0.00 20.00
late:JJ 20.50   0.00   9.94 12.50 12.50 20.50 12.13 20.50 12.50
1980s:NNS 20.50   9.94   0.00 10.17 15.50 20.50   8.91 20.50   9.62
when:WRB 10.00 20.50 20.50 20.00 20.00 10.00 20.00 10.00 20.00
Hungary:NNP 20.50 12.50   9.92   7.47 15.50 20.50   6.58 20.50   9.49
led:VBD 20.00 12.50 15.27 15.00 10.00 20.00 14.00 20.00 15.00
the:DT   0.00 20.50 20.50 20.00 20.00   0.00 20.00   0.00 20.00
reform_movement:NN 20.00 12.50   9.62   9.35 15.00 20.00   7.68 20.00   0.00
eastern:JJ 20.00   9.71 12.50 12.00 12.00 20.00 10.87 20.00 12.00
Europe:NNP 20.50 12.50   9.58   9.55 15.50 20.50   7.56 20.50   9.04
that:WDT 10.00 20.50 20.50 20.00 20.00 10.00 20.00 10.00 20.00
broke:VBD 20.00 10.06 12.91 15.00 10.00 20.00 14.47 20.00 15.00
the:DT   0.00 20.50 20.50 20.00 20.00   0.00 20.00   0.00 20.00
communist:JJ 20.00 10.50 10.75 12.00 12.00 20.00 11.79 20.00 12.00
monopoly:NN 20.00 12.50   9.02   9.58 15.00 20.00   8.20 20.00   8.98
political:JJ 20.00 10.50 10.40 12.00 12.00 20.00 10.74 20.00 12.00
power:NN 20.00 12.50   8.29   8.76 15.00 20.00   6.50 20.00   7.71
ushered:VBD 20.00 11.67 13.78 15.00 10.00 20.00 13.60 20.00 15.00
the:DT   0.00 20.50 20.50 20.00 20.00   0.00 20.00   0.00 20.00
possibility:NN 20.00 11.24   9.85   9.53 15.00 20.00   8.09 20.00   8.90
multiparty:JJ 20.00 10.48 11.89 12.00 12.00 20.00 12.00 20.00 12.00
politics:NNS 20.00 12.50   7.00   9.56 15.00 20.00   8.13 20.00   8.95
NO_WORD   1.00   9.00 10.00 10.00   1.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.40 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  0.44 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "broke" of "reform_movement" dropped on aligned hyp word "reform_movement"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1980
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "center" aligned badly to "focus"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "reform_movement" <-dobj-- "led" vs. hyp "reform_movement" <-prep_of-- "center", which aligned to text "focus" args have different parents, different relations: text "reform_movement" <-nsubj-- "broke" vs. hyp "reform_movement" <-prep_of-- "center", which aligned to text "focus"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.2412
Threshold: -1.8794


Inference ID: 2166

Txt: In Wednesday's filing, Google said it planned to complete the IPO "as soon as practicable."

Hyp: It's unclear whether Wednesday's twist will affect the timing of Google's initial public offering. (don't know)

It
PRP
's
VBZ
unclear
JJ
whether
IN
Wednesday
NNP
twist
NN
will
MD
affect
VB
the
DT
timing
NN
Google
NNP
initial_public_offering
NN
Wednesday:NNP 12.50 15.50 12.50 20.50   0.00   9.51 20.50 15.50 20.50   8.36 10.50   9.84
filing:NN 12.00 14.78 11.09 20.00   8.76   9.01 20.00 14.23 20.00   7.86 10.50   9.34
Google:NNP 12.50 15.50 12.50 20.50 10.50 10.50 20.50 15.50 20.50 10.50   0.00 10.50
said:VBD 15.00   9.03 10.88 20.00 15.50 15.00 20.00   9.54 20.00 14.21 15.50 15.00
it:PRP   0.00 15.00 15.00 20.00 12.50 12.00 20.00 15.00 20.00 12.00 12.50 12.00
planned:VBD 15.00   6.86   8.51 20.00 15.50 14.21 20.00   9.21 20.00 12.39 15.50 15.00
to:TO 20.00 20.00 20.00 20.00 20.50 20.00 10.00 20.00 10.00 20.00 20.50 20.00
complete:VB 15.00   8.59   8.59 20.00 15.50 13.61 20.00   8.57 20.00 11.43 15.50 15.00
the:DT 20.00 20.00 20.00 20.00 20.50 20.00 10.00 20.00   0.00 20.00 20.50 20.00
IPO:NN 12.00 15.00 12.00 20.00   9.84   7.91 20.00 15.00 20.00   9.11 10.50   0.00
as:RB 15.00 20.00 12.00 20.00 15.50 15.00 20.00 20.00 20.00 15.00 15.50 15.00
soon:RB 15.00 19.18   9.67 20.00 15.50 13.94 20.00 19.22 20.00 12.40 15.50 15.00
as:RB 15.00 20.00 12.00 20.00 15.50 15.00 20.00 20.00 20.00 15.00 15.50 15.00
practicable:JJ 15.00 11.15   9.80 20.00 12.50 10.42 20.00 10.76 20.00 11.64 12.50 12.00
NO_WORD 10.00   1.00   9.00   1.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.77 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  8.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1000
-1.00  1.00 NullPunisher.other : timing
-1.00  1.00 NullPunisher.other : unclear
-0.10  1.00 NullPunisher.article : the
-0.05  1.00 NullPunisher.aux : will
-1.00  1.00 NullPunisher.other : affect
-0.10  1.00 NullPunisher.functionWord : whether
-1.00  1.00 NullPunisher.other : It
-0.05  1.00 NullPunisher.aux : 's
-2.00  1.00 RootEntailment.unalignedRoot : "unclear" not aligned to anything
Hand-tuned score (dot product of above): -4.7561
Threshold: -1.8794


Inference ID: 2174

Txt: Al-Jazeera broadcasts to millions of Arab viewers from headquarters in Doha, Qatar.

Hyp: Al-Jazeera has occasionally run into problems with authorities in other Arab countries. (don't know)

Al-Jazeera
NNP
has
VBZ
occasionally
RB
run
VBN
problems
NNS
authorities
NNS
other
JJ
Arab
JJ
countries
NNS
Al-Jazeera:NNP   0.00 15.50 15.50 15.50   9.07   8.53 12.50 12.50   7.88
broadcasts:VBZ 15.50 10.00 18.32   9.25 15.00 13.88 12.00 12.00 15.00
millions:NNS   9.63 15.00 14.77 14.12   8.35   7.81 12.00 12.00   7.66
Arab:JJ 12.50 12.00 12.00 12.00 12.00 12.00 10.00   0.00 12.00
viewers:NNS   9.80 15.00 12.60 14.57   8.64   6.96 12.00 12.00   7.97
headquarters:NN   9.30 15.00 15.00 13.78   8.36   7.77 12.00 12.00   7.09
Doha:NNP 10.00 15.50 15.50 15.50 10.50 10.50 12.50 12.50 10.50
Qatar:NNP 10.00 15.50 15.50 15.50 10.50 10.50 12.50 12.50 10.50
NO_WORD 10.00   1.00   9.00 10.00 10.00 10.00   9.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.42 Alignment.score
 1.00  0.80 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.22 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added other[other-JJ]
-1.00  1.00 NullPunisher.other : run
-0.05  1.00 NullPunisher.aux : has
-1.00  1.00 NullPunisher.other : other
-1.00  1.00 NullPunisher.other : authorities
-1.00  1.00 NullPunisher.other : occasionally
-1.00  1.00 NullPunisher.other : problems
-2.00  1.00 RootEntailment.unalignedRoot : "run" not aligned to anything
Hand-tuned score (dot product of above): -7.2084
Threshold: -1.8794


Inference ID: 818

Txt: The new study refutes earlier findings by researchers at the University of California, Los Angeles, who concluded that the odds of getting head and neck cancers rose in tandem with the frequency and duration of marijuana use.

Hyp: The latest findings contradict a California study that implicated regular pot smoking as having markedly higher risks for head and neck cancers. (yes)

The
DT
latest
JJS
findings
NNS
contradict
VB
a
DT
California
NNP
study
NN
that
WDT
implicated
VBN
regular
JJ
pot
NN
smoking
NN
as
IN
having
VBG
markedly
RB
higher
JJR
risks
NNS
head
NN
neck
NN
cancers
NNS
The:DT   0.00 20.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
new:JJ 20.00   8.96 12.00 12.00 20.00 12.50 12.00 20.00 11.64   8.69 12.00 11.67 20.00 11.41 11.71   9.76 12.00 10.04 11.88 12.00
study:NN 20.00 10.23   3.25 13.57 20.00   8.70   0.00 20.00 14.75 11.51   8.98   4.68 20.00 15.00 14.06 11.72   7.39   8.48   9.07   3.59
refutes:VBZ 20.00 10.31 14.97   8.82 20.00 15.50 13.15 20.00   8.03 10.57 15.00 13.65 20.00 10.00 20.00 12.00 14.66 15.00 15.00 13.33
earlier:JJR 20.00   7.24 12.00 11.88 20.00 12.50 11.59 20.00 10.97   9.38 12.00 11.19 20.00 11.28 10.28   9.11 12.00 11.63 11.50 11.77
findings:NNS 20.00 10.02   0.00 11.87 20.00   9.38   3.25 20.00 12.66 10.28   9.18   6.92 20.00 15.00 12.59 11.86   6.91   9.09   9.51   5.12
researchers:NNS 20.00 10.23   3.47 13.71 20.00   8.50   1.44 20.00 14.03 11.87   8.74   5.06 20.00 15.00 13.14 11.70   6.27   8.30   7.72   1.96
the:DT   0.00 20.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
University_of_California:NNP 20.50 12.50   8.75 15.50 20.50   5.50   8.98 20.50 15.50 12.50   9.54   9.52 20.50 15.50 15.50 12.50   8.99   9.06   9.63   9.16
Los_Angeles:NNP 20.50 12.50 10.50 15.50 20.50 10.00 10.50 20.50 15.50 12.50 10.50 10.50 20.50 15.50 15.50 12.50 10.50 10.50 10.50 10.50
who:WP 20.00 15.00 12.00 15.00 20.00 12.50 12.00 20.00 15.00 15.00 12.00 12.00 20.00 15.00 20.00 15.00 12.00 12.00 12.00 12.00
concluded:VBD 20.00   9.30   8.18   5.10 20.00 15.50 10.24 20.00   7.30 11.25 14.57 12.94 20.00   9.46 16.54 11.66 12.72 14.42 15.00 11.71
that:IN 20.00 20.00 20.00 20.00 20.00 20.50 20.00   0.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
the:DT   0.00 20.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
odds:NNS 20.00 10.59   8.81 13.72 20.00   9.23   8.89 20.00 14.32 12.00   8.97   8.52 20.00 13.28 13.94 11.22   7.45   8.95   9.42   7.81
of:IN 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
getting:VBG 20.00 12.00 15.00 10.00 20.00 15.50 15.00 20.00 10.00 10.60 13.44 14.45 20.00   3.87 20.00 12.00 13.09 13.20 13.66 15.00
head:NN 20.00 12.00   9.09 15.00 20.00   8.35   8.48 20.00 14.62 11.42   8.73   9.02 20.00 14.76 15.00 12.00   8.08   0.00   3.96   8.67
neck:NN 20.00 12.00   9.51 15.00 20.00   9.09   9.07 20.00 15.00 12.00   7.11   8.84 20.00 13.45 14.76 12.00   8.13   3.96   0.00   7.81
cancers:NNS 20.00 12.00   5.12 13.89 20.00   8.91   3.59 20.00 12.67 12.00   9.13   4.61 20.00 14.84 13.77 11.15   6.47   8.67   7.81   0.00
rose:VBD 20.00 11.24 15.00   9.64 20.00 15.50 14.96 20.00   9.82 11.86 14.00 15.00 20.00   9.86 19.45 10.48 15.00 14.91 15.00 15.00
tandem:NN 20.00 12.00   9.54 15.00 20.00   9.15   9.12 20.00 13.74 11.32   5.58   9.50 20.00 15.00 15.00   9.04   8.83   8.89   9.33   9.26
the:DT   0.00 20.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
frequency:NN 20.00 11.85   8.08 13.77 20.00   9.41   7.58 20.00 14.28 11.49   9.47   7.55 20.00 15.00 13.23   9.00   7.68   8.91   7.94   6.06
duration:NN 20.00 11.40   7.86 14.99 20.00   8.41   8.12 20.00 14.66   7.47   8.77   8.74 20.00 13.55 13.07   9.43   7.04   8.22   8.87   8.24
marijuana:NN 20.00 12.00   6.09 14.39 20.00 10.50   6.67 20.00 11.21 12.00   8.23   4.07 20.00 14.42 14.50 11.45   7.94 10.00   8.19   4.79
use:NN 20.00 10.76   8.63 14.85 20.00   8.14   6.22 20.00 14.25   9.18   8.48   8.54 20.00 13.98 15.00 10.77   7.52   7.97   8.68   8.10
NO_WORD   1.00   9.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00   9.00 10.00 10.00 10.00 10.00   9.00   9.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.72 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  13.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.15 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added higher[higher-JJR]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "new" of "study" dropped on aligned hyp word "study"
 0.00  1.00 NegPolarity.hypNegWord : "latest": tag "JJS" is in NegPolarityMarkers list
-0.05  1.00 NullPunisher.aux : having
-1.00  1.00 NullPunisher.other : regular
-1.00  1.00 NullPunisher.other : risks
-1.00  1.00 NullPunisher.other : contradict
-0.10  1.00 NullPunisher.functionWord : as
-1.00  1.00 NullPunisher.other : implicated
-1.00  1.00 NullPunisher.other : higher
-0.10  1.00 NullPunisher.article : The
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : smoking
-1.00  1.00 NullPunisher.other : markedly
-1.00  1.00 NullPunisher.other : pot
-3.00  1.00 NullPunisher.entity : California
 1.00  1.00 Quant.contract : [the,a]
-2.00  1.00 RootEntailment.unalignedRoot : "contradict" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -11.7689
Threshold: -1.8794


Inference ID: 820

Txt: Kessler's team conducted 60,643 face-to-face interviews with adults in 14 countries.

Hyp: Kessler's team interviewed more than 60,000 adults in 14 countries. (yes)

Kessler
NNP
team
NN
interviewed
VBD
more_than
IN
60,000
CD
adults
NNS
14
CD
countries
NNS
Kessler:NNP   0.00 10.50 15.50 20.50 20.50 10.50 20.50 10.50
team:NN 10.50   0.00 14.74 20.00 20.05   6.94 20.32   6.82
conducted:VBD 15.50 14.89   4.95 20.00 19.94 13.95 20.14 15.00
60,643:CD 20.50 20.50 20.50 20.50   5.00 20.50   5.00 20.50
face-to-face:JJ 12.50 12.00 12.00 20.00 20.50 12.00 20.50 12.00
interviews:NNS 10.50   8.41   0.00 20.00 19.43   7.68 19.76   7.58
adults:NNS 10.50   6.94 13.82 20.00 20.50   0.00 19.66   5.00
14:CD 20.50 20.32 20.50 20.50   5.00 19.66   0.00 20.50
countries:NNS 10.50   6.82 15.00 20.00 20.50   5.00 20.50   0.00
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.71 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.88 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "face-to-face" of "interviews" dropped on aligned hyp word "interviewed"
-1.00  1.00 NullPunisher.other : more_than
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "team" <-nsubj-- "conducted" vs. hyp "team" <-nsubj-- "interviewed", which aligned to text "interviews" args have different parents but same relations: text "countries" <-prep_in-- "conducted" vs. hyp "countries" <-prep_in-- "interviewed", which aligned to text "interviews" args have different parents, different relations: text "adults" <-prep_with-- "conducted" vs. hyp "adults" <-dobj-- "interviewed", which aligned to text "interviews"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.9638
Threshold: -1.8794


Inference ID: 806

Txt: Investigators described the gunman as white, about 30 years old, of medium build with shoulder-length, wavy blond hair and as having a "scruffy" or "grunge" appearance.

Hyp: The gunman is described as a white male in his 30s with a medium build, shoulder-length blonde hair and an unkempt appearance. (yes)

The
DT
gunman
NN
is
VBZ
described
VBN
a
DT
white
JJ
male
NN
his
PRP$
30s
NNS
a
DT
medium
NN
build
VBN
shoulder-length
JJ
blonde
JJ
hair
NN
an
DT
unkempt
JJ
appearance
NN
Investigators:NNS 20.00   8.32 15.00 15.00 20.00 12.00   8.36 12.00   9.67 20.00   7.26 15.00 12.00 12.00   7.77 20.00 12.00   8.00
described:VBN 20.00 11.80 10.00   0.00 20.00   9.55 13.32 15.00 13.76 20.00 14.66 10.00 12.00 12.00 14.10 20.00 11.24 11.52
the:DT   0.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00
gunman:NN 20.00   0.00 15.00 11.80 20.00   9.55   8.32 12.00   8.36 20.00   8.01 15.00 11.81   9.43   8.15 20.00 11.30   8.62
as:RB 20.00 15.00 20.00 20.00 20.00 12.00 15.00 15.00 15.00 20.00 15.00 20.00 12.00 12.00 15.00 18.17 12.00 15.00
white:JJ 20.00   9.55 12.00   9.55 20.00   0.00   8.99 15.00 11.06 20.00 10.92 12.00 10.00   8.58   9.37 20.00   6.52 10.99
about:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
30:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 10.50 20.50 20.14 20.50 19.06 20.50 20.50 20.50 20.50 20.50
years:NNS 20.00   8.68 15.00 13.79 20.00 11.76   8.59 12.00   7.75 20.00   6.94 12.68 12.00 11.86   7.49 20.00 12.00   6.14
old:JJ 20.00 12.00 12.00 11.69 20.00   8.15 12.00 15.00 11.09 20.00 11.62 12.00   9.23 10.00 11.25 20.00   9.44 12.00
medium:NN 20.00   8.01 15.00 14.66 20.00 10.92   7.90 12.00   9.22 20.00   0.00 15.00 10.65 10.37   6.54 20.00 11.92   6.84
build:VBN 20.00 15.00 10.00 10.00 20.00 12.00 15.00 15.00 15.00 20.00 15.00   0.00 12.00 12.00 15.00 20.00 12.00 15.00
shoulder-length:JJ 20.00 11.81 12.00 12.00 20.00 10.00 10.77 15.00 10.99 20.00 10.65 12.00   0.00   8.89   9.02 20.00   8.74 10.84
wavy:JJ 20.00 11.01 12.00 12.00 20.00   7.98   8.09 15.00 10.11 20.00 11.12 12.00   7.49   5.06   5.41 20.00   5.63 10.51
blond:JJ 20.00   9.40 12.00   9.08 20.00   7.10   6.12 15.00   8.18 20.00 10.64 12.00   8.80   1.00   4.14 20.00   6.23 11.48
hair:NN 20.00   8.15 15.00 14.10 20.00   9.37   3.58 12.00   7.16 20.00   6.54 15.00   9.02   4.89   0.00 20.00   6.79   7.40
as:RB 20.00 15.00 20.00 20.00 20.00 12.00 15.00 15.00 15.00 20.00 15.00 20.00 12.00 12.00 15.00 18.17 12.00 15.00
having:VBG 20.00 15.00 10.00   9.18 20.00 12.00 13.78 15.00 11.09 20.00 14.65 10.00   9.43 11.66 14.07 20.00   9.76 14.17
a:DT 10.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00   8.73 20.00 20.00
scruffy:JJ 20.00 10.67 12.00   9.97 20.00   7.72 11.10 15.00   9.99 20.00 12.00 11.86 10.00   6.93 10.17 20.00   6.16 12.00
``:`` 10.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 19.16 10.00 19.04 19.67 19.83 19.07 20.00 10.00 20.00 20.00
grunge:NN 20.00   9.52 15.00 15.00 20.00 11.85   9.13 12.00   7.54 20.00   8.44 15.00 12.00   7.53   7.48 20.00 10.60   7.92
appearance:NN 20.00   8.62 15.00 11.52 20.00 10.99   8.52 12.00   8.85 20.00   6.84 15.00 10.84 11.33   7.40 20.00 11.13   0.00
NO_WORD   1.00 10.00   1.00 10.00   1.00   9.00 10.00 10.00 10.00   1.00 10.00 10.00   9.00   9.00 10.00   1.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.26 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.22 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "as" of "hair" dropped on aligned hyp word "hair"
-0.10  1.00 NullPunisher.article : an
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.article : a
-0.10  1.00 NullPunisher.article : a
-0.10  1.00 NullPunisher.functionWord : his
 1.00  1.00 Quant.contract : [a,an]
-1.00  1.00 Structure.relMismatch : text "medium" is prep_of of "described" while hyp "medium" is prep_with of "described" which aligned to text "described"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.7386
Threshold: -1.8794


Inference ID: 823

Txt: The body of Satomi Mitarai was found by a teacher after her attacker returned to class in bloody clothes.

Hyp: Mitarai's body was found by a teacher after her killer returned to their classroom covered in blood. (yes)

Mitarai
NNP
body
NN
was
VBD
found
VBN
a
DT
teacher
NN
her
PRP$
killer
NN
returned
VBN
their
PRP$
classroom
NN
covered
VBN
blood
NN
The:DT 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
body:NN 10.50   0.00 15.00 10.98 20.00   7.64 12.00   6.98 15.00 12.00   8.42 11.77   3.53
Satomi_Mitarai:NNP   5.00 10.50 15.50 15.50 20.50 10.50 12.50 10.50 15.50 12.50 10.50 15.50 10.50
was:VBD 15.50 15.00   0.00 10.00 20.00 15.00 15.00 15.00 10.00 15.00 15.00 10.00 15.00
found:VBN 15.50 10.98 10.00   0.00 20.00 14.78 15.00 12.09   5.56 15.00 14.39   6.37   9.31
a:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
teacher:NN 10.50   7.64 15.00 14.78 20.00   0.00 12.00   8.22 13.11 12.00   1.33 15.00   8.80
after:IN 20.50 20.00 20.00 20.00 18.51 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
her:PRP$ 12.50 12.00 15.00 15.00 20.00 12.00   0.00 12.00 15.00   6.15 12.00 15.00 12.00
attacker:NN 10.50   5.90 15.00 10.23 20.00   8.16 12.00   4.93 12.86 12.00   9.10 13.79   4.89
returned:VBD 15.50 15.00 10.00   5.56 20.00 13.11 15.00 14.95   0.00 15.00 14.10   9.04 15.00
class:NN 10.50   6.67 15.00 15.00 20.00   5.82 12.00   7.90 15.00 12.00   5.00 15.00   8.01
bloody:JJ 12.50   7.00 12.00   8.67 20.00 12.00 15.00   5.31 10.38 15.00 12.00 10.93   5.64
clothes:NNS 10.50   7.37 15.00 13.87 20.00   8.36 12.00   8.75 15.00 12.00   8.41 13.47   8.64
NO_WORD 10.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.18 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.46 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added covered[covered-VBN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "clothes" of "returned" dropped on aligned hyp word "returned"
-1.00  1.00 NullPunisher.other : killer
-0.10  1.00 NullPunisher.functionWord : her
-0.10  1.00 NullPunisher.functionWord : their
-1.00  1.00 NullPunisher.other : covered
 1.00  1.00 Quant.contract : [a,a]
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.1201
Threshold: -1.8794


Inference ID: 2147

Txt: Witnesses told police a gas canister exploded to start the fire in a multilevel building which houses the Ycua Bolanos supermarket, a food court and a parking garage, causing the bottom floor to collapse.

Hyp: Television footage showed several levels of the multi-level supermarket covered in soot, including the lower level parking garage. (don't know)

Television
NNP
footage
NNP
showed
VBD
several
JJ
levels
NNS
the
DT
multi-level
JJ
supermarket
NN
covered
VBN
soot
NN
including
VBG
the
DT
lower
JJR
level
NN
parking
NN
garage
NN
Witnesses:NNS   8.76   9.18 15.00 12.00   8.49 20.00 12.00   9.10 15.00   9.38 15.00 20.00 12.00   8.49   9.47   9.15
told:VBD 15.50 15.00   8.14 12.00 15.00 20.00 12.00 15.00   9.76 15.00   9.85 20.00 12.00 15.00 13.86 13.05
police:NNS   8.48   8.85 14.79 12.00   7.64 20.00 12.00   8.91 14.62   8.87 13.77 20.00 12.00   7.64   6.42   8.08
a:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
gas:NN   8.70   9.42 14.37 12.00   7.06 20.00 11.65   9.06 14.63   7.67 14.17 20.00   9.97   7.88   9.12   9.28
canister:NN 10.50   6.16 14.56 12.00   8.21 20.00 11.47   7.60 12.52   6.70 14.90 20.00 12.00   8.95   7.85   7.76
exploded:VBD 15.50 14.14   8.43 12.00 14.94 20.00 12.00 14.80   7.94 12.10   9.06 20.00 12.00 15.00 11.60 12.20
to:TO 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
start:VB 15.50 15.00   9.40 12.00 13.97 20.00 10.88 15.00 10.00 14.60 10.00 20.00 12.00 13.19 15.00 15.00
the:DT 20.50 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
fire:NN   9.08   8.26 13.55 12.00   8.30 20.00 12.00   8.79 11.27   5.78 14.17 20.00 12.00   8.30   4.70   5.93
a:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
multilevel:JJ 12.50 12.00 12.00 10.00   7.00 20.00   1.90 10.03 11.22 10.80 11.96 20.00   9.19   7.00 10.34   9.20
building:NN   6.29   8.01 15.00 12.00   6.83 20.00 12.00   5.85 14.01   6.89 15.00 20.00 11.16   6.83   3.17   3.46
which:WDT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
houses:VBZ 15.50 15.00 10.00 12.00 14.07 20.00 12.00 11.15   8.73 12.85   7.71 20.00 10.73 15.00 10.12   9.00
the:DT 20.50 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
Ycua_Bolanos:NNP 10.50 10.50 15.50 12.50 10.50 20.50 12.50 10.50 15.50 10.50 15.50 20.50 12.50 10.50 10.50 10.50
supermarket:NN   8.47   8.57 13.58 12.00   8.63 20.00 12.00   0.00 12.70   8.89 15.00 20.00 12.00   8.63   6.66   6.07
a:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
food:NN   6.84   8.35 12.40 12.00   6.52 20.00 12.00   4.79 13.06   6.66 14.73 20.00 10.76   6.52   8.27   8.10
court:NN   8.42   9.27 15.00 12.00   7.57 20.00 11.17   8.87 14.17   9.36 15.00 20.00 10.23   7.57   8.94   9.11
a:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
parking:NN   9.44   9.76 15.00 12.00   8.13 20.00 12.00   6.66 12.25   7.76 14.71 20.00 11.20   8.13   0.00   3.20
garage:NN   8.73   8.63 15.00 12.00   8.90 20.00 12.00   6.07 12.81   7.25 15.00 20.00 11.12   8.90   3.20   0.00
causing:VBG 15.50 15.00   7.71 12.00   9.82 20.00 11.43 15.00   7.18 11.36   8.28 20.00   9.26 13.73 14.07 14.61
the:DT 20.50 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
bottom:JJ 12.50 11.58 11.72 10.00   9.25 20.00   7.40 10.25   9.17 11.42 12.00 20.00   9.85 10.17 10.51 11.15
floor:NN   7.82   8.44 14.99 12.00   8.10 20.00 12.00   8.27 12.61   7.18 14.73 20.00 10.37   8.10   7.20   5.09
collapse:NN   8.94   9.54 14.08 12.00   8.15 20.00 12.00   9.22 14.97   8.98 14.77 20.00 12.00   8.15   9.28   9.42
NO_WORD 10.00 10.00 10.00   9.00 10.00   1.00   9.00 10.00 10.00 10.00 10.00   1.00   9.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.59 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  10.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.19 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added lower[lower-JJR]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Ycua_Bolanos" of "supermarket" dropped on aligned hyp word "supermarket"
-1.00  1.00 NullPunisher.other : levels
-3.00  1.00 NullPunisher.entity : Television
-1.00  1.00 NullPunisher.other : multi-level
-1.00  1.00 NullPunisher.other : soot
-1.00  1.00 NullPunisher.other : several
-1.00  1.00 NullPunisher.other : covered
-1.00  1.00 NullPunisher.other : lower
-1.00  1.00 NullPunisher.other : showed
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : footage
 1.00  1.00 Quant.contract : [a,the]
-2.00  1.00 RootEntailment.unalignedRoot : "showed" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -11.3609
Threshold: -1.8794


Inference ID: 2156

Txt: Gross domestic product, a measure of total output within the nation's borders, climbed at a 3% annual pace in the April-June period.

Hyp: Annual rate increase of 3% in second quarter much lower than forecasts. (don't know)

Annual
JJ
rate
NN
increase
NN
3
CD
%
NN
second
JJ
quarter
NN
much
RB
lower
JJR
forecasts
NNS
Gross_domestic_product:NN 12.00   7.64   5.79 18.82 10.17 10.55   8.01 15.00 12.00   8.68
a:DT 20.00 20.00 20.00 20.50 20.50 20.50 20.50 20.00 20.00 20.00
measure:NN 12.00   7.89   7.47 20.50 10.50 11.31   8.35 15.00 10.52   8.61
total:JJ 10.00 12.00   8.66 17.48 11.09 10.22 12.50 12.00 10.00 12.00
output:NN 12.00   6.71   5.27 19.49 10.50   9.68   7.95 15.00 12.00   6.70
the:DT 20.00 20.00 20.00 20.50 20.50 20.50 20.50 20.00 20.00 20.00
nation:NN 12.00   7.24   7.11 18.83 10.45 10.55   7.69 15.00 12.00   8.09
borders:NNS 12.00   8.74   8.66 20.50 10.19 11.89   9.21 15.00 12.00   9.25
climbed:VBD 12.00 14.20 12.30 17.94 15.00 11.67 15.09 20.00 10.84 12.70
a:DT 20.00 20.00 20.00 20.50 20.50 20.50 20.50 20.00 20.00 20.00
3:CD 20.50 18.83 19.64   0.00 20.00 19.39 20.01 20.50 20.50 19.95
%:NN 12.50 10.16   9.38 20.00   0.00 12.32 10.50 15.50 12.50 10.50
annual:JJ   0.00   8.02   6.38 17.97 12.20   9.15 10.03 12.00   9.69 10.18
pace:NN 12.00   5.98   5.46 18.92 10.50   8.09   6.90 15.00 11.47   7.29
the:DT 20.00 20.00 20.00 20.50 20.50 20.50 20.50 20.00 20.00 20.00
April-June:JJ 10.00 12.00 12.00 20.50 12.50 10.50 12.50 12.00 10.00 12.00
period:NN 12.00   6.21   3.58 19.46   9.53   7.36   3.58 15.00 12.00   6.38
NO_WORD   9.00 10.00 10.00 10.00 10.00   9.00 10.00   9.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.36 Alignment.score
 1.00  0.80 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  7.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.30 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added forecasts[forecasts-NNS]
-2.00  1.00 Date.hypDateIns : hypothesis date insertion: quarter
-1.00  1.00 NullPunisher.other : rate
-3.00  1.00 NullPunisher.entity : second
-1.00  1.00 NullPunisher.other : much
-1.00  1.00 NullPunisher.other : increase
-1.00  1.00 NullPunisher.other : forecasts
-3.00  1.00 NullPunisher.entity : quarter
-1.00  1.00 NullPunisher.other : lower
-2.00  1.00 RootEntailment.unalignedRoot : "increase" not aligned to anything
-1.00  1.00 Structure.relMismatch : noun args have different parents but same relations: "Annual": "pace" vs. "increase"
Hand-tuned score (dot product of above): -16.3196
Threshold: -1.8794


Inference ID: 1989

Txt: Intel has decided to push back the launch date for its 4-GHz Pentium 4 desktop processor to the first quarter of 2005.

Hyp: Intel would raise the clock speed of the company's flagship Pentium 4 processor to 4 GHz. (don't know)

Intel
NNP
would
MD
raise
VB
the
DT
clock
NN
speed
NN
the
DT
company
NN
flagship
JJ
Pentium
NNP
4
CD
processor
NN
4
CD
GHz
NNS
Intel:NNP   0.00 20.50 15.50 20.50 10.50 10.50 20.50   9.01 12.50   9.63 20.50   9.75 20.50 10.50
has:VBZ 15.50 18.39 10.00 20.00 15.00 15.00 20.00 15.00 12.00 15.00 20.50 15.00 20.50 15.00
decided:VBN 15.50 20.00   7.49 20.00 12.91 15.00 20.00 15.00   9.85 15.00 20.50 14.49 20.50 15.00
to:TO 20.50 10.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00 20.00 20.50 20.00 20.50 20.00
push_back:VB 15.50 20.00 10.00 20.00 15.00 15.00 20.00 15.00 12.00 15.00 20.50 15.00 20.50 15.00
the:DT 20.50 10.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.50 20.00
launch:NN 10.50 20.00 14.88 20.00   7.47   7.57 20.00   8.24   8.78 10.00 20.50   8.83 20.50 10.00
date:NN 10.50 20.00 14.66 20.00   9.03   8.09 20.00   7.50 12.00 10.00 19.69   8.80 19.69 10.00
its:PRP$ 12.50 20.00 15.00 20.00 12.00 12.00 20.00 12.00 15.00 12.00 20.50 12.00 20.50 12.00
4-GHz:JJ 12.50 20.00 12.00 20.00 12.00 12.00 20.00 12.00 10.00 12.00 20.50 12.00 20.50   2.00
Pentium:NNP   9.63 20.00 15.00 20.00 10.00 10.00 20.00 10.00 12.00   0.00 20.50   8.02 20.50 10.00
4:CD 20.50 20.50 19.75 20.50 18.61 20.41 20.50 19.83 20.50 20.50   0.00 18.65   0.00 20.50
desktop:NN 10.50 20.00 15.00 20.00   8.58   7.19 20.00   7.75 10.02   9.25 20.17   4.66 20.17 10.00
processor:NN   9.75 20.00 15.00 20.00   8.28   6.69 20.00   6.78 11.02   8.02 18.65   0.00 18.65 10.00
the:DT 20.50 10.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.50 20.00
first_quarter:NN 10.50 20.50 15.50 20.50 10.30   9.52 20.50   9.38 12.50 10.50 20.50 10.30 20.50 10.50
2005:CD 20.50 20.50 20.50 20.50 19.35 20.30 20.50 20.50 20.50 20.50   8.93 20.17   8.93 20.50
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00   9.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.84 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added GHz[GHz-NNS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "first_quarter" of "processor" dropped on aligned hyp word "processor"
-1.00  1.00 NullPunisher.other : company
-0.05  1.00 NullPunisher.aux : would
-1.00  1.00 NullPunisher.other : GHz
-3.00  1.00 NullPunisher.entity : 4
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "raise" aligned badly to "decided"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Intel" <-xsubj-- "push_back" vs. hyp "Intel" <-nsubj-- "raise", which aligned to text "decided" args have different parents but same relations: text "date" <-dobj-- "push_back" vs. hyp "speed" <-dobj-- "raise", which aligned to text "decided"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -11.5176
Threshold: -1.8794


Inference ID: 829

Txt: The last Australian to win Miss Universe was Perth's Kerry Anne Wells in 1972.

Hyp: Not since 1972 has an Australian woman been named Miss Universe. (yes)

Not
RB
since
IN
1972
CD
has
VBZ
an
DT
Australian
JJ
woman
NN
been
VBN
named
VBN
Miss
NNP
Universe
NNP
The:DT 20.00 20.00 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00
last:JJ 12.00 20.00 20.50 12.00 20.00 10.00 12.00 12.00 12.00 12.00 12.00
Australian:JJ 12.00 20.00 20.50 12.00 20.00   0.00 12.00 12.00 12.00 12.00 12.00
to:TO 20.00 20.00 20.50 20.00   8.20 20.00 20.00 20.00 20.00 20.00 20.00
win:VB 20.00 20.00 18.42 10.00 20.00 12.00 15.00 10.00 10.00 15.00 15.00
Miss:NNP 15.00 20.00 20.50 15.00 20.00 12.00   1.68 15.00 15.00   0.00   7.87
Universe:NNP 15.00 20.00 20.50 15.00 20.00 12.00   6.95 15.00 15.00   7.87   0.00
was:VBD 20.00 20.00 20.50 10.00 20.00 12.00 15.00   0.00 10.00 15.00 15.00
Perth:NNP 15.50 20.50 20.50 15.50 20.50 12.50   9.55 15.50 15.50   9.86   9.69
Kerry_Anne_Wells:NNP 15.50 20.50 20.50 15.50 20.50 12.50   7.88 15.50 15.50   8.72   8.60
1972:CD 20.50 20.50   0.00 20.50 20.50 20.50 20.41 20.50 18.57 20.50 20.50
NO_WORD   9.00 10.00 10.00 10.00   1.00   9.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.88 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  7.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.18 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added named[named-VBN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "win" of "Australian" dropped on aligned hyp word "Australian"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1972
-1.00  1.00 NullPunisher.other : named
-0.05  1.00 NullPunisher.aux : been
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : since
-0.05  1.00 NullPunisher.aux : has
-1.00  1.00 NullPunisher.other : woman
-1.00  1.00 NullPunisher.other : Not
-2.00  1.00 RootEntailment.unalignedRoot : "has" not aligned to anything
-1.00  1.00 Structure.relMismatch : noun args have different parents but same relations: "Australian": "Kerry_Anne_Wells" vs. "woman"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -5.4369
Threshold: -1.8794


Inference ID: 723

Txt: The city continued to grow through much of the 20th century.

Hyp: The city continued to grow, but its services deteriorated. (don't know)

The
DT
city
NN
continued
VBD
to
TO
grow
VB
its
PRP$
services
NNS
deteriorated
VBD
The:DT   0.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
city:NN 20.00   0.00 13.89 20.00 14.64 12.00   7.09 14.43
continued:VBD 20.00 13.89   0.00 20.00   9.72 15.00 14.87   6.65
to:TO 10.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
grow:VB 20.00 14.64   9.72 20.00   0.00 15.00 14.68   7.77
through:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
much:RB 20.00 15.00 20.00 20.00 20.00 15.00 15.00 20.00
the:DT   0.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
20th:JJ 20.50 12.50 11.69 20.50 11.42 15.50 12.50 11.45
century:NN 20.50   8.41 13.06 20.50 13.98 12.50   9.07 14.25
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.00 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : services
-1.00  1.00 NullPunisher.other : deteriorated
-0.10  1.00 NullPunisher.functionWord : its
-3.00  1.00 Structure.argsMismatch : deteriorated (verbal compl of the root) not aligned
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -6.3756
Threshold: -1.8794


Inference ID: 693

Txt: This growth proved short-lived, for a Swedish invasion ( 1655-56 ) devastated the flourishing city of Warsaw.

Hyp: Warsaw was invaded by the Swedes in 1655, and the city was devastated. (yes)

Warsaw
NNP
was
VBD
invaded
VBN
the
DT
Swedes
NNS
1655
CD
the
DT
city
NN
was
VBD
devastated
VBN
This:DT 20.50 20.00 20.00 10.00 20.00 20.50 10.00 20.00 20.00 20.00
growth:NN   9.25 15.00 15.00 20.00   9.30 20.50 20.00   6.94 15.00 14.66
proved:VBD 15.50 10.00   8.06 20.00 15.00 20.50 20.00 14.63 10.00   7.20
short-lived:JJ 12.50 12.00 10.16 20.00 12.00 20.50 20.00 11.11 12.00   9.64
a:DT 20.50 20.00 20.00 10.00 20.00 20.50 10.00 20.00 20.00 20.00
Swedish:JJ 12.50 12.00 12.00 20.00   7.00 20.50 20.00 12.00 12.00 12.00
invasion:NN 10.20 15.00   2.50 20.00   9.90 20.50 20.00   8.76 15.00 12.94
1655-56:CD 20.50 20.50 20.50 20.50 20.50   5.50 20.50 20.50 20.50 20.50
devastated:VBN 15.50 10.00   5.66 20.00 15.00 20.50 20.00 12.22 10.00   0.00
the:DT 20.50 20.00 20.00   0.00 20.00 20.50   0.00 20.00 20.00 20.00
flourishing:JJ 12.50 12.00 11.68 20.00 12.00 20.50 20.00 10.17 12.00 10.15
city:NN   4.25 15.00 15.00 20.00   8.60 20.50 20.00   0.00 15.00 12.22
Warsaw:NNP   0.00 15.50 15.50 20.50   9.89 20.50 20.50   4.25 15.50 15.50
NO_WORD 10.00   1.00 10.00   1.00 10.00 10.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.12 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "flourishing" of "city" dropped on aligned hyp word "city"
-3.00  1.00 Date.dateHeadMismatch : 1655 vs. 1655-56
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : invaded
-0.05  1.00 NullPunisher.aux : was
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.unalignedRoot : "invaded" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.1410
Threshold: -1.8794


Inference ID: 826

Txt: A car bomb exploded outside a U.S. military base near Baji.

Hyp: A car bomb exploded outside a U.S. military base near Beiji. (yes)

A
DT
car_bomb
NN
exploded
VBD
a
DT
U.S.
NNP
military
JJ
base
NN
Beiji
NNP
A:DT   0.00 20.00 20.00   0.00 20.50 20.00 20.00 20.50
car_bomb:NN 20.00   0.00 15.00 20.00 10.50 12.00 10.00 10.50
exploded:VBD 20.00 15.00   0.00 20.00 15.50 10.00 14.13 15.50
a:DT   0.00 20.00 20.00   0.00 20.50 20.00 20.00 20.50
U.S.:NNP 20.50 10.50 15.50 20.50   0.00 12.50   7.97 10.00
military:JJ 20.00 12.00 10.00 20.00 12.50   0.00   8.83 12.50
base:NN 20.00 10.00 14.13 20.00   7.97   8.83   0.00 10.50
Baji:NNP 20.50 10.50 15.50 20.50 10.00 12.50 10.50 10.00
NO_WORD   1.00 10.00 10.00   1.00 10.00   9.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.59 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  0.88 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Beiji[Beiji-NNP]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Baji" of "exploded" dropped on aligned hyp word "exploded"
-3.00  1.00 NullPunisher.entity : Beiji
 1.00  1.00 Quant.contract : [a,a]
 1.00  1.00 Quant.contract : [a,a]
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.2113
Threshold: -1.8794


Inference ID: 803

Txt: Because of Reagan's economic strategy, the federal budget deficit ballooned.

Hyp: Reagan's economic strategy led to huge federal budget deficits. (yes)

Reagan
NNP
economic
JJ
strategy
NN
led
VBD
huge
JJ
federal
JJ
budget_deficits
NNS
Reagan:NNP   0.00 12.50   8.85 15.50 12.50 12.50 10.50
economic:JJ 12.50   0.00 11.57 11.32   8.76   8.71 12.00
strategy:NN   8.85 11.57   0.00 15.00   8.08 12.00 10.00
the:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.00
federal:JJ 12.50   8.71 12.00 11.46 10.00   0.00 12.00
budget_deficit:NN 10.50   7.43 10.00 15.00 12.00 12.00   1.00
ballooned:VBD 15.50 10.85 15.00 10.00   9.09 11.71 15.00
NO_WORD 10.00   9.00 10.00 10.00   9.00   9.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.63 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.43 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added huge[huge-JJ]
-1.00  1.00 NullPunisher.other : huge
-1.00  1.00 NullPunisher.other : led
-2.00  1.00 RootEntailment.unalignedRoot : "led" not aligned to anything
Hand-tuned score (dot product of above): -2.2998
Threshold: -1.8794


Inference ID: 719

Txt: Once called the "Queen of the Danube," Budapest has long been the focal point of the nation and a lively cultural centre.

Hyp: Budapest was once popularly known as the "Queen of the Danube." (yes)

Budapest
NNP
was
VBD
once
RB
popularly
RB
known
VBN
the
DT
Queen
NNP
the
DT
Danube
NNP
Once:IN 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.50
called:VBD 15.50 10.00 20.00 19.57   4.30 20.00 15.00 20.00 15.50
the:DT 20.50 20.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
Queen:NNP   9.89 15.00 15.00 15.00 15.00 20.00   0.00 20.00   9.97
the:DT 20.50 20.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
Danube:NNP   9.47 15.50 15.50 15.50 15.50 20.50   9.97 20.50   0.00
Budapest:NNP   0.50 15.50 15.50 15.50 15.50 20.50   9.89 20.50   9.97
has:VBZ 15.50 10.00 20.00 20.00 10.00 20.00 15.00 20.00 15.50
long:RB 15.50 20.00 10.00   9.01 16.89 20.00 15.00 20.00 15.50
been:VBN 15.50   0.00 20.00 20.00 10.00 20.00 10.00 20.00 15.50
the:DT 20.50 20.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
focal_point:NN   8.33 15.00 15.00 15.00 15.00 20.00   9.34 20.00   9.92
the:DT 20.50 20.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
nation:NN   9.33 15.00 15.00 12.65 12.67 20.00   8.82 20.00   9.94
a:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50
lively:JJ 12.50 12.00 12.00 12.00 10.32 20.00 12.00 20.00 12.50
cultural:JJ 12.50 12.00 12.00 10.48   9.30 20.00 12.00 20.00 12.50
center:NN   4.46 15.00 15.00 15.00 13.59 20.00   8.35 20.00   9.62
NO_WORD 10.00   1.00   9.00   9.00 10.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.08 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.56 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added popularly[popularly-RB]
-1.00  1.00 NullPunisher.other : popularly
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : known
-2.00  1.00 RootEntailment.unalignedRoot : "known" not aligned to anything
Hand-tuned score (dot product of above): -2.9221
Threshold: -1.8794


Inference ID: 1806

Txt: Vanunu, 49, was abducted by Israeli agents and convicted of treason in 1986 after discussing his work as a mid-level Dimona technician with Britain's Sunday Times newspaper.

Hyp: Vanunu's disclosures in 1968 led experts to conclude that Israel has a stockpile of nuclear warheads. (don't know)

Vanunu
NNP
disclosures
NNS
1968
CD
led
VBD
experts
NNS
to
TO
conclude
VB
that
IN
Israel
NNP
has
VBZ
a
DT
stockpile
NN
nuclear_warheads
NNS
Vanunu:NNP   0.50 10.50 20.50 15.50 10.50 20.50 15.50 20.50 10.50 15.50 20.50 10.50 10.50
49:CD 20.50 20.50   8.65 19.88 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
was:VBD 15.50 15.00 20.50 10.00 15.00 20.00 10.00 20.00 15.50 10.00 20.00 15.00 15.00
abducted:VBN 15.50 14.83 20.13   9.55 14.41 20.00 10.00 20.00 15.50 10.00 20.00 15.00 15.00
Israeli:JJ 12.50 12.00 20.50 12.00 12.00 20.00 12.00 20.00   5.50 12.00 20.00 12.00 12.00
agents:NNS 10.50   7.65 20.50 15.00   5.81 20.00 15.00 20.00   8.81 15.00 20.00   7.66 10.00
convicted:VBN 15.50 13.03 19.33   9.29 13.12 20.00   9.18 20.00 15.50 10.00 20.00 15.00 15.00
treason:NN 10.50   9.69 19.68 13.92   8.93 20.00 13.69 20.00 10.50 15.00 20.00   9.74 10.00
1986:CD 20.50 20.28   2.82 19.27 20.50 20.50 20.50 20.50 20.50 20.50 20.50 19.66 20.50
after:IN 20.50 20.00 20.50 20.00 20.00 17.32 20.00 10.00 20.50 20.00 18.51 20.00 20.00
discussing:VBG 15.50 12.77 20.50   8.73 12.92 20.00   5.91 20.00 15.50 10.00 20.00 15.00 15.00
his:PRP$ 12.50 12.00 20.50 15.00 12.00 20.00 15.00 20.00 12.50 15.00 20.00 12.00 12.00
work:NN 10.50   5.31 20.33 15.00   5.64 20.00 14.78 20.00   8.16 15.00 20.00   6.24 10.00
a:DT 20.50 20.00 20.50 20.00 20.00 10.00 20.00 20.00 20.50 20.00   0.00 20.00 20.00
mid-level:JJ 12.50 12.00 19.37 12.00 12.00 20.00 11.42 20.00 12.50 12.00 20.00 10.87 12.00
Dimona:NNP 10.50 10.50 20.50 15.50 10.50 20.50 15.50 20.50 10.50 15.50 20.50 10.50 10.50
technician:NN 10.50   8.86 19.93 15.00   7.52 20.00 15.00 20.00   9.76 15.00 20.00   8.84 10.00
Britain:NNP 10.00   8.73 20.50 15.50   7.46 20.50 15.50 20.50   5.20 15.50 20.50   8.43 10.50
Sunday_Times:NNP 10.50   8.95 20.50 15.50   8.28 20.50 15.50 20.50   9.55 15.50 20.50   8.68 10.50
newspaper:NN 10.50   7.99 19.35 15.00   6.46 20.00 15.00 20.00   8.83 15.00 20.00   7.68 10.00
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.28 Alignment.score
 1.00  0.78 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  9.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.15 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added nuclear_warheads[nuclear_warheads-NNS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "49" of "Vanunu" dropped on aligned hyp word "Vanunu"
-3.00  1.00 Date.dateHeadMismatch : 1968 vs. 1986
-0.10  1.00 NullPunisher.functionWord : that
-1.00  1.00 NullPunisher.other : led
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : has
-1.00  1.00 NullPunisher.other : experts
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : nuclear_warheads
-1.00  1.00 NullPunisher.other : conclude
-1.00  1.00 NullPunisher.other : stockpile
-2.00  1.00 RootEntailment.unalignedRoot : "led" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -9.8741
Threshold: -1.8794


Inference ID: 2119

Txt: Pakistani officials announced that two South African men in their custody had confessed to planning attacks at popular tourist spots in their home country.

Hyp: Two South Africans were arrested in Pakistan in the past week after a shoot-out at a house between al-Qaeda operatives and security forces. (don't know)

Two
CD
South
NNP
Africans
NNPS
were
VBD
arrested
VBN
Pakistan
NNP
the
DT
past
JJ
week
NN
a
DT
shoot-out
NN
a
DT
house
NN
al-Qaeda
NNP
operatives
NNS
security_forces
NNS
Pakistani:JJ 20.50 12.00 12.00 12.00 12.00   4.85 20.00 10.00 12.00 20.00 12.00 20.00 12.00 12.50 12.00 12.00
officials:NNS 20.50   7.15   7.13 15.00 12.24   8.72 20.00 12.00   6.98 20.00   9.18 20.00   6.06   8.33   6.55 10.00
announced:VBD 20.50 15.00 15.00 10.00   8.91 15.50 20.00 11.81 11.78 20.00 15.00 20.00 14.03 15.50 15.00 15.00
that:IN 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00
two:CD   0.00 20.50 20.50 20.50 19.49 20.50 20.50 13.05 18.56 20.50 20.50 20.50 19.78 20.50 20.50 20.50
South_African:NNS 20.50   5.00   0.98 15.00 15.00   9.89 20.00 12.00   9.05 20.00   9.93 20.00   8.57   9.95   8.42 10.00
men:NNS 20.50   7.55   8.14 15.00 11.95   9.02 20.00 11.42   6.83 20.00   9.11 20.00   6.73   6.69   7.69 10.00
their:PRP$ 20.50 12.00 12.00 15.00 15.00 12.50 20.00 15.00 12.00 20.00 12.00 20.00 12.00 12.50 12.00 12.00
custody:NN 20.50   9.07   9.39 15.00   9.58 10.08 20.00 12.00   8.65 20.00   9.83 20.00   8.59   9.66   9.14 10.00
had:VBD 20.50 15.00 15.00 10.00 10.00 15.50 20.00 12.00 15.00 20.00 15.00 20.00 15.00 15.50 15.00 15.00
confessed:VBD 20.50 15.00 15.00 10.00   3.19 15.50 20.00 10.72 14.13 20.00 15.00 20.00 14.59 15.50 12.75 15.00
planning:NN 20.50   8.79   9.17 15.00 14.45   9.90 20.00 11.16   8.31 20.00   8.76 20.00   8.24   9.40   8.88 10.00
attacks:NNS 20.50   8.19   8.68 15.00 11.03   9.49 20.00 10.85   7.58 20.00   8.62 20.00   7.50   8.82   4.80 10.00
popular:JJ 20.50 12.00 12.00 12.00 11.64 12.50 20.00   9.76 11.65 20.00 12.00 20.00 12.00 12.50   9.01 12.00
tourist:NN 20.50   8.60   8.34 15.00 14.00   9.77 20.00 12.00   8.49 20.00   9.78 20.00   7.88   9.54   8.21 10.00
spots:NNS 20.50   9.20   9.49 15.00 13.92 10.16 20.00 10.34   8.81 20.00   9.87 20.00   8.76   9.78   5.72 10.00
their:PRP$ 20.50 12.00 12.00 15.00 15.00 12.50 20.00 15.00 12.00 20.00 12.00 20.00 12.00 12.50 12.00 12.00
home:NN 20.50   6.50   8.33 15.00 13.65   7.91 20.00 12.00   7.63 20.00   9.46 20.00   6.96   8.86   7.90 10.00
country:NN 20.50   4.70   7.46 15.00 14.00   5.06 20.00 11.31   6.58 20.00   8.99 20.00   5.80   8.00   6.92 10.00
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00   1.00   9.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.52 Alignment.score
 1.00  0.82 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  13.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.12 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added operatives[operatives-NNS]
 1.00  1.00 Hypernym.posWiden : widening in positive context: south_african -> Africans
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : were
-3.00  1.00 NullPunisher.entity : al-Qaeda
-1.00  1.00 NullPunisher.other : security_forces
-1.00  1.00 NullPunisher.other : arrested
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : past
-1.00  1.00 NullPunisher.other : South
-1.00  1.00 NullPunisher.other : shoot-out
-1.00  1.00 NullPunisher.other : house
-1.00  1.00 NullPunisher.other : week
-1.00  1.00 NullPunisher.other : operatives
-2.00  1.00 RootEntailment.unalignedRoot : "arrested" not aligned to anything
Hand-tuned score (dot product of above): -13.1937
Threshold: -1.8794


Inference ID: 810

Txt: Meadows scored a bit part in a January episode of "Law & Order".

Hyp: Meadows appeared in a "Law & Order" episode which aired in January. (yes)

Meadows
NNP
appeared
VBD
a
DT
Law
NNP
Order
NNP
episode
NN
which
WDT
aired
VBN
January
NNP
Meadows:NNP   0.00 15.00 20.00   9.40   9.37   9.60 20.00 15.00 10.06
scored:VBD 15.00   8.27 20.00 15.00 15.00 14.81 20.00   9.64 15.50
a:DT 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00 20.50
bit_part:NN 10.00 15.00 20.00 10.00 10.00 10.00 20.00 15.00 10.50
a:DT 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00 20.50
January:NNP 10.06 15.50 20.50   8.24   8.85   9.27 20.50 15.50   0.00
episode:NN   9.60   9.74 20.00   8.48   8.43   0.00 20.00   7.83   9.27
Law:NNP   9.40 15.00 20.00   0.00   8.00   8.48 20.00 15.00   8.24
Order:NNP   9.37 15.00 20.00   8.00   0.00   8.43 20.00 15.00   8.85
NO_WORD 10.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.29 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.44 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added aired[aired-VBN]
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1000
-0.10  1.00 NullPunisher.functionWord : which
-1.00  1.00 NullPunisher.other : aired
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "appeared" aligned badly to "bit_part"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Meadows" <-nsubj-- "scored" vs. hyp "Meadows" <-nsubj-- "appeared", which aligned to text "bit_part" args have different parents but same relations: text "episode" <-prep_in-- "scored" vs. hyp "episode" <-prep_in-- "appeared", which aligned to text "bit_part"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -5.9605
Threshold: -1.8794


Inference ID: 809

Txt: Authorities say Monica Meadows, who has appeared in catalogs and magazines, is in stable condition.

Hyp: Monica Meadows is in stable condition. (yes)

Monica_Meadows
NNP
is
VBZ
stable
JJ
condition
NN
Authorities:NNS   8.57 15.00 12.00   5.51
say:VBP 15.50 10.00 12.00 13.19
Monica_Meadows:NNP   0.50 15.50 12.50   9.17
who:WP 12.50 15.00 15.00 12.00
has:VBZ 15.50   8.64 12.00 15.00
appeared:VBN 15.50 10.00 11.86 12.64
catalogs:NNS   9.88 15.00 11.98   7.77
magazines:NNS   9.75 15.00 12.00   6.94
is:VBZ 15.50   0.00 12.00 15.00
stable:JJ 12.50 12.00   0.00 10.55
condition:NN   9.17 15.00 10.55   0.00
NO_WORD 10.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.81 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "appeared" of "Monica_Meadows" dropped on aligned hyp word "Monica_Meadows"
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: say-VBP
-2.00  1.00 Location.mismatch : no clear info of matching: be(X, prep_in)
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.7274
Threshold: -1.8794


Inference ID: 733

Txt: According to tradition, they founded Tenochtitlan ( "Place of the High Priest Tenoch" ) after much wandering when they saw on an island in Lake Texcoco the sign that their god Huitzilopochtli had indicated -- an eagle perched on a cactus, eating a serpent .

Hyp: They saw an eagle perched on a cactus eating a serpent. (yes)

They
PRP
saw
VBD
an
DT
eagle
NN
perched
VBN
on
IN
a
DT
cactus
NNS
eating
VBG
a
DT
serpent
NN
tradition:NN 12.00 14.51 20.00   7.54 13.51 20.00 20.00   7.50 14.44 20.00   7.64
they:PRP   0.00 15.00 20.00 12.00 15.00 20.00 20.00 12.00 15.00 20.00 12.00
founded:VBD 15.00   9.19 20.00 12.98   8.98 20.00 20.00 15.00 10.00 20.00 15.00
Tenochtitlan:NNP 12.50 15.50 20.50 10.50 15.50 20.50 20.50 10.50 15.50 20.50 10.50
Place:VB 15.00 10.00 20.00 15.00 10.00 20.00 20.00 15.00 10.00 20.00 15.00
the:DT 20.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00
High_Priest:NNP 12.00 15.00 20.00 10.00 15.00 20.00 20.00 10.00 15.00 20.00 10.00
Tenoch:NNP 12.00 15.00 20.00 10.00 15.00 20.00 20.00 10.00 15.00 20.00 10.00
after:IN 20.00 20.00 20.00 20.00 20.00 10.00 18.51 20.00 20.00 18.51 20.00
much:RB 15.00 20.00 20.00 15.00 20.00 20.00 20.00 15.00 20.00 20.00 15.00
wandering:VBG 15.00   6.48 20.00 14.30   5.43 20.00 20.00 12.52   6.33 20.00 11.50
when:WRB 20.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00
they:PRP   0.00 15.00 20.00 12.00 15.00 20.00 20.00 12.00 15.00 20.00 12.00
saw:VBD 15.00   0.00 20.00 13.07   8.75 20.00 20.00 15.00   9.08 20.00 14.24
an:DT 20.00 20.00   0.00 20.00 20.00 18.21   8.73 20.00 20.00   8.73 20.00
island:NN 12.00 14.08 20.00   8.28 11.97 20.00 20.00   6.77 13.73 20.00   8.68
Lake_Texcoco:NNP 12.50 15.50 20.50   9.35 15.50 20.50 20.50   9.82 15.50 20.50   9.65
the:DT 20.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00
sign:NN 12.00 14.60 20.00   9.02 14.47 20.00 20.00   7.94 15.00 20.00   9.29
that:IN 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00
their:PRP$   0.00 15.00 20.00 12.00 15.00 20.00 20.00 12.00 15.00 20.00 12.00
god:NN 12.00 12.81 20.00   8.74 15.00 20.00 20.00   9.24 12.50 20.00   7.71
Huitzilopochtli:NNP 12.50 15.50 20.50 10.50 15.50 20.50 20.50 10.50 15.50 20.50 10.50
had:VBD 15.00 10.00 20.00 15.00 10.00 20.00 20.00 15.00 10.00 20.00 15.00
indicated:VBN 15.00   8.55 20.00 15.00 10.00 20.00 20.00 15.00 10.00 20.00 15.00
an:DT 20.00 20.00   0.00 20.00 20.00 18.21   8.73 20.00 20.00   8.73 20.00
eagle:NN 12.00 13.07 20.00   0.00 13.95 20.00 20.00   7.73 12.86 20.00   5.68
perched:VBN 15.00   8.75 20.00 13.95   0.00 20.00 20.00 10.92   7.83 20.00 11.75
a:DT 20.00 20.00   8.73 20.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00
cactus:NNS 12.00 15.00 20.00   7.73 10.92 20.00 20.00   0.00 11.86 20.00   6.61
eating:VBG 15.00   9.08 20.00 12.86   7.83 20.00 20.00 11.86   0.00 20.00 12.75
a:DT 20.00 20.00   8.73 20.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00
serpent:NN 12.00 14.24 20.00   5.68 11.75 20.00 20.00   6.61 12.75 20.00   0.00
NO_WORD 10.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.38 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.73 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "island" of "saw" dropped on aligned hyp word "saw"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-1.00  1.00 NullPunisher.other : on
-2.00  1.00 Person.mismatch : person mimatch between They and they
 1.00  1.00 Quant.contract : [an,an]
 1.00  1.00 Quant.contract : [a,a]
 1.00  1.00 Quant.contract : [a,a]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "eagle" <-dobj-- "indicated" vs. hyp "eagle" <-dobj-- "saw", which aligned to text "saw" args have different parents but same relations: text "eagle" <-nsubjpass-- "perched" vs. hyp "eagle" <-dobj-- "saw", which aligned to text "saw"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.2275
Threshold: -1.8794


Inference ID: 2158

Txt: Prosecutors were still expected to unseal an 11-count manslaughter indictment against the ferry's captain, Michael Gansas. He violated procedure by his absence from the wheelhouse during docking, when Smith lost control of the ferry.

Hyp: Mr Smith entered his guilty plea to 11 counts of manslaughter and also to lying to investigators about his medical history, under an agreement reached with prosecutors. (don't know)

Mr_Smith
NNP
entered
VBD
his
PRP$
guilty
JJ
plea
NN
11
CD
counts
NNS
manslaughter
NN
also
RB
to
TO
lying
VBG
investigators
NNS
his
PRP$
medical_history
NN
an
DT
agreement
NN
reached
VBN
prosecutors
NNS
Prosecutors:NNS   7.05 15.00 12.00 12.00   9.21 20.50   9.08 10.00 15.00 20.00 15.00   8.03 12.00 10.00 20.00   7.87 15.00   0.00
were:VBD 15.50 10.00 15.00 12.00 15.00 20.50 15.00 15.00 20.00 20.00 10.00 15.00 15.00 15.00 20.00 15.00 10.00 15.00
still:RB 15.50 20.00 15.00 12.00 15.00 20.50 15.00 15.00 10.00 20.00 20.00 15.00 15.00 15.00 20.00 15.00 20.00 15.00
expected:VBN 15.50   9.41 15.00 12.00 14.84 18.95 15.00 14.12 20.00 20.00 10.00 15.00 15.00 15.00 20.00 13.22   8.36 15.00
to:TO 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00   8.20 20.00 20.00 20.00
unseal:VB 15.50   9.33 15.00   6.77   9.15 20.08 11.79 12.01 20.00 20.00   7.91 12.99 15.00 15.00 20.00 14.56   9.38   9.56
an:DT 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00   8.20 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00
11-count:JJ 12.50 12.00 15.00   8.26 11.00 14.83   2.00 11.21 12.00 20.00 11.03 11.58 15.00 12.00 20.00 12.00 12.00 11.30
manslaughter:NN 10.50 13.12 12.00   4.94   4.52 20.42   5.20   0.00 15.00 20.00 11.84   6.61 12.00 10.00 20.00 10.00 13.45   3.30
indictment:NN   8.64 13.88 12.00   4.56   2.97 20.25   4.49   6.50 15.00 20.00 12.06   4.31 12.00 10.00 20.00   6.98 13.92   2.98
the:DT 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00
ferry:NN   8.62 14.03 12.00 12.00   9.63 19.29   9.55 10.00 15.00 20.00 12.65   8.71 12.00 10.00 20.00   8.68 14.94   9.12
captain:NN   8.32 13.99 12.00 11.00   9.68 20.50   9.60   8.65 15.00 20.00 12.80   7.12 12.00 10.00 20.00   8.79 13.95   6.01
Michael_Gansas:NNP   7.60 15.50 12.50 12.50   9.60 20.50   9.46 10.50 15.50 20.50 15.50   9.26 12.50 10.50 20.50   8.17 15.50   9.36
He:PRP 12.50 15.00   0.00 15.00 12.00 20.50 12.00 12.00 20.00 20.00 15.00 12.00   0.00 12.00 20.00 12.00 15.00 12.00
violated:VBD 15.50   7.89 15.00   7.77   9.99 20.09 12.46 13.63 20.00 20.00   8.39 12.41 15.00 15.00 20.00 13.46   8.46 11.13
procedure:NN   7.01 14.47 12.00 10.07   7.25 19.48   8.27   6.19 15.00 20.00 13.66   8.00 12.00 10.00 20.00   6.59 14.72   7.81
his:PRP$ 12.50 15.00   0.00 15.00 12.00 20.50 12.00 12.00 20.00 20.00 15.00 12.00   0.00 12.00 20.00 12.00 15.00 12.00
absence:NN   8.76 13.22 12.00 12.00   8.74 19.69   9.34 10.00 15.00 20.00 14.97   9.19 12.00 10.00 20.00   8.31 13.47   9.23
the:DT 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00
wheelhouse:NN 10.50 13.89 12.00 11.10   9.82 20.50   9.59   9.99 15.00 20.00 12.31 10.00 12.00 10.00 20.00 10.00 15.00   9.25
docking:NN 10.50 15.00 12.00 12.00   9.96 19.16   9.14 10.00 15.00 20.00 14.07   9.16 12.00 10.00 20.00   9.75 14.70 10.00
when:WRB 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00
Smith:NNP   5.00 15.50 12.50 12.50   9.66 20.50   9.53 10.50 15.50 20.50 15.50   8.44 12.50 10.50 20.50   8.28 15.50   8.58
lost:VBD 15.50   6.44 15.00 10.21 14.00 19.79 12.85 13.52 20.00 20.00 10.00 14.85 15.00 15.00 20.00 14.73   8.71 13.97
control:NN   7.63 14.08 12.00 12.00   8.83 20.26   8.08 10.00 15.00 20.00 13.91   8.38 12.00 10.00 20.00   6.31 15.00   8.56
the:DT 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00
ferry:NN   8.62 14.03 12.00 12.00   9.63 19.29   9.55 10.00 15.00 20.00 12.65   8.71 12.00 10.00 20.00   8.68 14.94   9.12
NO_WORD 10.00 10.00 10.00   9.00 10.00 10.00 10.00 10.00   9.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.55 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  12.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.22 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added reached[reached-VBN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "wheelhouse" of "absence" dropped on aligned hyp word "plea"
-1.00  1.00 NullPunisher.other : reached
-1.00  1.00 NullPunisher.other : investigators
-1.00  1.00 NullPunisher.other : agreement
-1.00  1.00 NullPunisher.other : entered
-0.10  1.00 NullPunisher.functionWord : to
-3.00  1.00 NullPunisher.entity : 11
-0.10  1.00 NullPunisher.functionWord : his
-1.00  1.00 NullPunisher.other : also
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : lying
-1.00  1.00 NullPunisher.other : medical_history
-1.00  1.00 NullPunisher.other : guilty
-2.00  1.00 RootEntailment.unalignedRoot : "entered" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -12.9033
Threshold: -1.8794


Inference ID: 804

Txt: The Golden Flyers set a school record this year with 16 victories.

Hyp: The Golden Flyers set a school record with 16 victories. (yes)

The
DT
Golden_Flyers
NNP
set
VBD
a
DT
school
NN
record
NN
16
CD
victories
NNS
The:DT   0.00 20.50 20.00 10.00 20.00 20.00 20.50 20.00
Golden_Flyers:NNP 20.50   0.00 15.50 20.50   8.58   6.40 20.50   8.73
set:VBD 20.00 15.50   0.00 20.00 14.10 10.54 20.50 14.79
a:DT 10.00 20.50 20.00   0.00 20.00 20.00 20.50 20.00
school:NN 20.00   8.58 14.10 20.00   0.00   7.64 19.83   8.43
record:NN 20.00   6.40 10.54 20.00   7.64   0.00 19.65   7.43
this:DT 10.00 20.50 20.00 10.00 20.00 20.00 20.50 20.00
year:NN 20.00   6.89 14.67 20.00   7.53   5.76 20.30   7.71
16:CD 20.50 20.50 20.50 20.50 19.83 19.65   0.00 20.50
victories:NNS 20.00   8.73 14.79 20.00   8.43   7.43 20.50   0.00
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.02 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  8.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "year" of "set" dropped on aligned hyp word "set"
 1.00  1.00 Quant.contract : [a,a]
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 5.8465
Threshold: -1.8794


Inference ID: 2159

Txt: The city ferry fleet shuttles about 70,000 people a day between Staten Island and Manhattan, a 5.2-mile trip across New York Harbor that takes about 25 minutes.

Hyp: The ferry taking commuters from Manhattan to Staten Island crashed into a pier. (don't know)

The
DT
ferry
NN
taking
VBG
commuters
NNS
Manhattan
NNP
Staten_Island
NNP
crashed
VBD
a
DT
pier
NN
The:DT   0.00 20.00 20.00 20.00 20.50 20.50 20.00 10.00 20.00
city:NN 20.00   7.45 14.80   5.04   5.47 10.50 14.84 20.00   7.74
ferry:NN 20.00   0.00 13.31   5.43   9.77 10.50 10.34 20.00   8.57
fleet:NN 20.00   9.16 14.74   8.89   9.66 10.50 13.46 20.00   9.50
shuttles:VBZ 20.00 11.63 10.00 10.20 15.50 15.50   6.98 20.00 14.49
about:RB 20.00 15.00 20.00 15.00 15.50 15.50 20.00 20.00 15.00
70,000:CD 20.50 18.95 19.99 18.36 20.50 20.50 18.59 20.50 19.78
people:NNS 20.00   8.42 13.80   6.29   8.03 10.50 13.04 20.00   8.23
a:DT 10.00 20.00 20.00 20.00 20.50 20.50 20.00   0.00 20.00
day:NN 20.00   8.88 12.72   7.53   8.65 10.50 13.85 20.00   8.73
Staten_Island:NNP 20.50 10.50 15.50 10.50 10.00   0.00 15.50 20.50 10.50
Manhattan:NNP 20.50   9.77 15.50   9.52   0.00 10.00 15.50 20.50   9.65
a:DT 10.00 20.00 20.00 20.00 20.50 20.50 20.00   0.00 20.00
5.2-mile:JJ 20.00 10.88 12.00   9.71 12.50 12.50 10.79 20.00 12.00
trip:NN 20.00   8.49 13.49   7.63   9.28 10.50 14.56 20.00   9.22
New_York_Harbor:NNP 20.50 10.50 15.50 10.50 10.00 10.00 15.50 20.50 10.50
that:WDT 10.00 20.00 20.00 20.00 20.50 20.50 20.00 10.00 20.00
takes:VBZ 20.00 15.00   0.00 13.43 15.50 15.50 10.00 20.00 15.00
about:RB 20.00 15.00 20.00 15.00 15.50 15.50 20.00 20.00 15.00
25:CD 20.50 18.92 18.65 19.78 20.50 20.50 20.18 20.50 20.36
minutes:NNS 20.00   7.64 13.59   7.93   9.25 10.50 12.91 20.00   9.20
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.77 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.22 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added pier[pier-NN]
-1.00  1.00 NullPunisher.other : pier
-0.10  1.00 NullPunisher.article : The
-1.00  1.00 NullPunisher.other : taking
-1.00  1.00 NullPunisher.other : crashed
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : commuters
-2.00  1.00 RootEntailment.unalignedRoot : "crashed" not aligned to anything
Hand-tuned score (dot product of above): -5.9585
Threshold: -1.8794


Inference ID: 713

Txt: Rozsa ( Rose ) Hill , the third hill near the river, lies north of Castle Hill.

Hyp: Rozsa Hill lies north of Castle Hill. (yes)

Rozsa_Hill
NNP
lies
VBZ
north
NN
Castle_Hill
NNP
Rozsa:NNP   5.00 15.50 10.50 10.50
Rose:NNP   9.49 15.00   8.91   9.64
Hill:NNP   0.00 15.50   9.15   5.50
the:DT 20.50 20.00 20.00 20.50
third:JJ 12.50 12.00 11.72 12.50
hill:NN   0.50 14.88   8.12   9.60
the:DT 20.50 20.00 20.00 20.50
river:NN   8.40 10.60   5.01   8.89
lies:VBZ 15.50   0.00 12.01 15.50
north:NN   9.15 12.01   0.00   9.53
Castle_Hill:NNP   9.60 15.50   9.53   0.00
NO_WORD 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.00 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "hill" of "Hill" dropped on aligned hyp word "Rozsa_Hill"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 4.4237
Threshold: -1.8794


Inference ID: 2177

Txt: Only 14 percent of U.S. mothers exclusively breast-feed their babies for the minimum recommended six months.

Hyp: There are many benefits from breast-feeding. (don't know)

There
EX
are
VBP
many
JJ
benefits
NNS
breast-feeding
NN
Only:RB 20.00 20.00 12.00 15.00 15.00
14:CD 20.50 20.50 20.50 20.14 20.50
percent:NN 20.50 15.50 12.50   9.10   9.81
U.S.:NNP 20.50 15.50 12.50   8.35   9.30
mothers:NNS 20.00 15.00 12.00   5.29   6.86
exclusively:RB 20.00 20.00 12.00 15.00 14.38
breast-feed:VBD 20.00 10.00 12.00 13.23   9.80
their:PRP$ 20.00 15.00 13.55 12.00 12.00
babies:NNS 20.00 15.00 12.00   6.90   5.36
the:DT 10.00 20.00 20.00 20.00 20.00
minimum:NN 20.00 15.00 12.00   5.14   9.10
recommended:VBN 20.00 10.00 12.00 14.30 13.44
six:CD 20.50 20.50 20.50 20.04 19.45
months:NNS 20.00 15.00 12.00   7.50   8.55
NO_WORD   1.00 10.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.13 Alignment.score
 1.00  0.76 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added many[many-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "minimum" of "breast-feed" dropped on aligned hyp word "breast-feeding"
-0.10  1.00 NullPunisher.functionWord : There
-1.00  1.00 NullPunisher.other : many
-0.05  1.00 NullPunisher.aux : are
-1.00  1.00 NullPunisher.other : benefits
-2.00  1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.5448
Threshold: -1.8794


Inference ID: 2118

Txt: Francis Deng believes the government will be receptive to the plan to deploy 1,800 peacekeeping troops.

Hyp: The AU said it may send a 2 000-strong peacekeeping force. (don't know)

The
DT
AU
NNP
said
VBD
it
PRP
may
MD
send
VB
a
DT
2
CD
000-strong
JJ
peacekeeping
NN
force
NN
Francis_Deng:NNP 20.50 10.50 15.50 12.50 20.50 15.50 20.50 20.50 12.50 10.50 10.50
believes:VBZ 20.00 15.00 10.00 15.00 20.00 10.00 20.00 20.50 12.00 15.00 14.25
the:DT   0.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50 20.00 20.00 20.00
government:NN 20.00   7.60 15.00 12.00 20.00 15.00 20.00 19.85 12.00   9.08   3.45
will:MD 10.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50 20.00 20.00 20.00
be:VB 20.00 15.00 10.00 15.00 20.00 10.00 20.00 20.50 12.00 15.00 15.00
receptive:JJ 20.00 12.00 10.94 15.00 20.00 12.00 20.00 19.87 10.00 12.00 12.00
the:DT   0.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50 20.00 20.00 20.00
plan:NN 20.00   7.59 15.00 12.00 20.00 13.52 20.00 19.92 12.00   8.91   5.97
to:TO 10.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50 20.00 20.00 20.00
deploy:VB 20.00 15.00 10.00 15.00 20.00   6.29 20.00 20.50 12.00   7.67   9.43
1,800:CD 20.50 20.50 20.50 20.50 20.50 18.93 20.50   5.00 20.50 20.13 17.21
peacekeeping:VBG 20.00 15.00 10.00 15.00 20.00   7.31 20.00 20.50 12.00   0.00   8.94
troops:NNS 20.00   8.94 15.00 12.00 20.00 12.22 20.00 20.50 12.00   1.24   4.38
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.30 Alignment.score
 1.00  0.79 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  10.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.09 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added 000-strong[000-strong-JJ]
-3.00  1.00 NullPunisher.entity : 2
-1.00  1.00 NullPunisher.other : it
-1.00  1.00 NullPunisher.other : AU
-1.00  1.00 NullPunisher.other : force
-0.10  1.00 NullPunisher.article : The
-1.00  1.00 NullPunisher.other : send
-1.00  1.00 NullPunisher.other : said
-1.00  1.00 NullPunisher.other : 000-strong
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : may
-2.00  1.00 RootEntailment.unalignedRoot : "said" not aligned to anything
Hand-tuned score (dot product of above): -12.0536
Threshold: -1.8794


Inference ID: 819

Txt: Recreational marijuana smokers are no more likely to develop oral cancer than nonusers.

Hyp: Smoking marijuana does not increase the risk of developing oral cancer. (yes)

Smoking
NN
marijuana
NN
does
VBZ
not
RB
increase
VB
the
DT
risk
NN
developing
VBG
oral_cancer
NN
Recreational:JJ 12.00 12.00 12.00 12.00 12.00 20.00 12.00 12.00 12.00
marijuana:NN 10.00   0.00 15.00 15.00 14.17 20.00   7.76 15.00 10.00
smokers:NNS   9.63   5.24 14.97 15.00 14.18 20.00   6.86 15.00 10.00
are:VBP 15.00 15.00 10.00 20.00 10.00 20.00 15.00 10.00 15.00
no:RB 15.00 15.00 20.00   8.00 20.00 20.00 15.00 20.00 15.00
more:RBR 15.00 15.00 20.00 10.00 20.00 20.00 15.00 20.00 15.00
likely:JJ 12.00 12.00   9.43 12.00   8.36 20.00   7.52 10.93 12.00
to:TO 20.00 20.00 17.95 20.00 20.00 10.00 20.00 20.00 20.00
develop:VB 15.00 15.00   9.79 20.00   7.16 20.00 13.81   0.00 15.00
oral_cancer:NN 10.00 10.00 14.90 15.00 15.00 20.00 10.00 15.00   0.00
nonusers:NNS 10.00   6.47 15.00 15.00 13.33 20.00   7.21 13.35 10.00
NO_WORD 10.00 10.00   1.00   9.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.74 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.22 Alignment.txtSpan
-6.00  1.00 Adjunct.diffPol : hyp and txt have different polarity
 0.00  1.00 NegPolarity.hypNegWord : "increase": has child with relation "neg"
 0.00  1.00 NegPolarity.hypNegRoot : "increase": has child with relation "neg"
-1.00  1.00 NullPunisher.other : Smoking
-1.00  1.00 NullPunisher.other : risk
-1.00  1.00 NullPunisher.other : increase
-0.05  1.00 NullPunisher.aux : does
-1.00  1.00 NullPunisher.other : not
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.unalignedRoot : "increase" not aligned to anything
Hand-tuned score (dot product of above): -10.9367
Threshold: -1.8794


Inference ID: 678

Txt: Coextensive with the metropolitan district of Jakarta Raya, it lies at the mouth of the Ciliwung ( Liwung River ) on the northwest coast of Java.

Hyp: Jakarta lies at the mouth of the Ciliwung ( Liwung River ) on the northwest coast of the island of Java. (yes)

Jakarta
NNP
lies
VBZ
the
DT
mouth
NN
the
DT
Ciliwung
NN
Liwung
NNP
River
NNP
the
DT
northwest
JJ
coast
NN
the
DT
island
NN
Java
NNP
Coextensive:VBN 15.50 10.00 20.00 15.00 20.00 15.00 15.00 15.00 20.00 12.00 15.00 20.00 15.00 15.00
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00
metropolitan:JJ 12.50 10.35 20.00 12.00 20.00 12.00 12.00 12.00 20.00   7.50   9.30 20.00   7.09 12.00
district:NN   7.03 14.97 20.00   7.09 20.00 10.00 10.00   5.23 20.00   9.99   6.78 20.00   5.67 10.00
Jakarta_Raya:NNP   0.00 15.50 20.50   9.93 20.50 10.50 10.50   9.78 20.50 12.50   9.91 20.50   9.84 10.50
it:PRP 12.50 15.00 20.00 12.00 20.00 12.00 12.00 12.00 20.00 15.00 12.00 20.00 12.00 12.00
lies:VBZ 15.50   0.00 20.00 13.60 20.00 15.00 15.00 15.00 20.00   9.50 11.95 20.00 11.33 15.00
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00
mouth:NN   9.93 13.60 20.00   0.00 20.00 10.00 10.00   5.83 20.00 11.69   8.94 20.00   8.33 10.00
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00
Ciliwung:NN 10.50 15.00 20.00 10.00 20.00   0.00   5.00 10.00 20.00 12.00 10.00 20.00 10.00 10.00
Liwung:NNP 10.50 15.00 20.00 10.00 20.00   5.00   0.00 10.00 20.00 12.00 10.00 20.00 10.00 10.00
River:NNP   9.78 15.00 20.00   5.83 20.00 10.00 10.00   0.00 20.00 12.00   7.84 20.00   6.95 10.00
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00
northwest:JJ 12.50   9.50 20.00 11.69 20.00 12.00 12.00 12.00 20.00   0.00   5.94 20.00   7.88 12.00
coast:NN   9.91 11.95 20.00   8.94 20.00 10.00 10.00   7.84 20.00   5.94   0.00 20.00   4.81 10.00
Java:NNP 10.00 15.00 20.00 10.00 20.00 10.00 10.00 10.00 20.00 12.00 10.00 20.00 10.00   0.00
NO_WORD 10.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00   1.00   9.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.61 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  11.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added island[island-NN]
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : island
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Jakarta_Raya" <-prep_of-- "district" vs. hyp "Jakarta" <-nsubj-- "lies", which aligned to text "lies"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -3.6099
Threshold: -1.8794


Inference ID: 2120

Txt: Six hostages in Iraq were freed.

Hyp: The four Jordanian hostages, kidnapped about a week ago, were freed. (don't know)

The
DT
four
CD
Jordanian
JJ
hostages
NNS
kidnapped
VBN
a
DT
week
NN
ago
IN
were
VBD
freed
VBN
Six:CD 20.50   5.00 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
hostages:NNS 20.00 20.50 12.00   0.00 11.30 20.00   8.56 20.00 15.00 11.65
Iraq:NNP 20.50 20.50 12.50   9.89 15.50 20.50   9.55 20.50 15.50 15.50
were:VBD 20.00 20.50 12.00 15.00 10.00 20.00 15.00 20.00   0.00 10.00
freed:VBN 20.00 18.66 12.00 11.65   3.99 20.00 14.27 17.83 10.00   0.00
NO_WORD   1.00 10.00   9.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.88 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added week[week-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Iraq" of "hostages" dropped on aligned hyp word "hostages"
-1.00  1.00 NullPunisher.other : Jordanian
-1.00  1.00 NullPunisher.other : week
-1.00  1.00 NullPunisher.other : ago
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : kidnapped
-0.10  1.00 NullPunisher.article : The
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '4.0' vs '6.0'
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -8.8301
Threshold: -1.8794


Inference ID: 2154

Txt: The GDP report showed growth in business outlays advanced at a solid 8.9% pace.

Hyp: The GDP was a disappointing report. (don't know)

The
DT
GDP
NNP
was
VBD
a
DT
disappointing
JJ
report
NN
The:DT   0.00 20.00 20.00 10.00 20.00 20.00
GDP:NNP 20.00   0.00 15.00 20.00 12.00   8.75
report:NN 20.00   8.75 15.00 20.00   7.99   0.00
showed:VBD 20.00 15.00 10.00 20.00   9.21   8.87
growth:NN 20.00   7.96 15.00 20.00   9.15   7.74
business:NN 20.00   8.55 15.00 20.00 10.19   6.73
outlays:NNS 20.00   9.39 15.00 20.00 12.00   8.19
advanced:VBD 20.00 15.00 10.00 20.00 10.59 15.00
a:DT 10.00 20.00 20.00   0.00 20.00 20.00
solid:JJ 20.00 12.00 12.00 20.00   5.39 11.33
8.9:CD 20.50 20.50 20.50 20.50 20.50 20.15
%:NN 20.50 10.50 15.50 20.50 11.95 10.50
pace:NN 20.00   9.02 15.00 20.00   9.57   6.10
NO_WORD   1.00 10.00   1.00   1.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.25 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added disappointing[disappointing-JJ]
-1.00  1.00 NullPunisher.other : disappointing
-0.05  1.00 NullPunisher.aux : was
-0.10  1.00 NullPunisher.article : a
-0.10  1.00 NullPunisher.article : The
 1.00  1.00 Quant.contract : [the,a]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "GDP" <-nsubj-- "showed vs. hyp "GDP" <-nsubj-- "report", which aligned to text "report"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -5.3678
Threshold: -1.8794


Inference ID: 816

Txt: Israel plans to increase the share of gas in its energy balance to 25 percent by 2025 from the current 1 percent.

Hyp: Currently the share of natural gas in Israel's energy system amounts to no more than one percent. (yes)

Currently
RB
the
DT
share
NN
natural_gas
NN
Israel
NNP
energy
NN
system
NN
amounts
VBZ
no
DT
more_than
IN
one
CD
percent
NN
Israel:NNP 15.50 20.50   8.67   9.04   0.00   8.91   8.83 15.50 20.50 20.50 20.50   9.46
plans:VBZ 20.00 20.00 15.00 15.00 15.50 14.70 13.35 10.00 20.00 20.00 20.50 15.19
to:TO 20.00 10.00 20.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.50
increase:VB 20.00 20.00 14.12 15.00 15.50 13.14 15.00   9.24 20.00 20.00 20.50 12.99
the:DT 20.00   0.00 20.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.50
share:NN 15.00 20.00   0.00   7.58   8.67   6.85   6.72 15.00 20.00 20.00 20.50   6.95
gas:NN 15.00 20.00   7.60   7.52   9.40   1.92   7.78 13.93 20.00 20.00 20.50   9.04
its:PRP$ 20.00 20.00 12.00 12.00 12.50 12.00 12.00 15.00 20.00 20.00 20.50 12.50
energy:NN 15.00 20.00   6.85   7.86   8.91   0.00   7.06 14.40 20.00 20.00 20.50   8.48
balance:NN 15.00 20.00   7.98   8.74   9.63   8.23   7.14 14.18 20.00 20.00 20.50   9.32
25:CD 20.50 20.50 17.89 20.50 20.50 19.94 20.13 18.93 20.50 20.50   5.00 20.00
percent:NN 15.50 20.50   6.95   9.04   9.46   8.48   8.39 15.50 20.50 20.50 20.00   0.00
2025:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.05 20.31 20.50 20.50 10.50 20.50
the:DT 20.00   0.00 20.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.50
current:JJ   1.00 20.00 10.78 12.00 12.50 11.74   9.02 12.00 20.00 20.00 20.50 11.30
1:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.18 17.89 20.50 20.50   5.00 19.77
percent:NN 15.50 20.50   6.95   9.04   9.46   8.48   8.39 15.50 20.50 20.50 20.00   0.00
NO_WORD   9.00   1.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.00 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-8.00  1.00 Antonym.samePol : matching polarity with antonyms: (prep) to & from
 0.00  1.00 NegPolarity.hypNegWord : "no": is in NegPolarityMarkers list
-1.00  1.00 NullPunisher.other : amounts
-1.00  1.00 NullPunisher.other : no
-1.00  1.00 NullPunisher.other : more_than
 1.00  1.00 Quant.contract : [the,one]
-2.00  1.00 RootEntailment.unalignedRoot : "amounts" not aligned to anything
Hand-tuned score (dot product of above): -9.6958
Threshold: -1.8794


Inference ID: 2115

Txt: A senior al-Qaeda operative who was said to be planning an attack on Heathrow Airport has been arrested.

Hyp: Despite rumors, the British Home Office denied any specific threat had been made to target Heathrow airport. (don't know)

rumors
NNS
the
DT
British_Home_Office
NNP
denied
VBD
any
DT
specific
JJ
threat
NN
had
VBD
been
VBN
made
VBN
to
TO
target
VB
Heathrow
NNP
airport
NN
A:DT 20.00 10.00 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
senior:JJ 11.69 20.00 12.50 12.00 20.00 10.00 11.47 12.00 12.00 11.64 20.00 11.74 12.50 12.00
al-Qaeda:NNP   8.97 20.00   8.33 15.00 20.00 12.00   8.91 15.00 15.00 15.00 20.00 15.00 10.50   8.90
operative:NN   7.59 20.00   9.24 12.04 20.00 12.00   8.05 15.00 15.00 11.72 20.00 15.00 10.50   8.43
who:WP 12.00 20.00 12.50 15.00 20.00 15.00 12.00 15.00 15.00 15.00 20.00 15.00 12.50 12.00
was:VBD 15.00 20.00 15.50 10.00 20.00 12.00 15.00 10.00   0.00 10.00 20.00 10.00 15.50 15.00
said:VBN 13.65 20.00 15.50   8.29 20.00 11.05 15.00 10.00 10.00   7.21 20.00   9.25 15.50 15.00
to:TO 20.00 10.00 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.50 20.00
be:VB 15.00 20.00 15.50 10.00 20.00 12.00 15.00 10.00   0.00 10.00 20.00 10.00 15.50 15.00
planning:VBG 14.44 20.00 15.50 10.00 20.00 10.42 15.00 10.00 10.00 10.00 20.00 10.00 15.50 12.47
an:DT 20.00 10.00 20.50 20.00   8.00 20.00 20.00 20.00 20.00 20.00   8.20 20.00 20.50 20.00
attack:NN   7.67 20.00   8.77 12.86 20.00 11.93   5.74 15.00 15.00 12.95 20.00 14.15 10.50   7.27
Heathrow:NNP 10.00 20.00 10.50 15.00 20.00 12.00 10.00 15.00 15.00 15.00 20.00 15.00   0.50 10.00
Airport:NNP   9.06 20.00   9.36 15.00 20.00 12.00   8.68 15.00 15.00 15.00 20.00 15.00 10.50   0.00
has:VBZ 15.00 20.00 15.50 10.00 20.00 12.00 15.00   0.00 10.00 10.00 20.00 10.00 15.50 15.00
been:VBN 15.00 20.00 15.50 10.00 20.00 12.00 15.00 10.00   0.00 10.00 20.00 10.00 15.50 15.00
arrested:VBN 13.40 20.00 15.50   6.02 20.00 12.00 15.00 10.00 10.00   9.31 20.00   8.80 15.50 12.52
NO_WORD 10.00   1.00 10.00 10.00 10.00   9.00 10.00   1.00   1.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.68 Alignment.score
 1.00  0.84 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  10.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added specific[specific-JJ]
-1.00  1.00 NullPunisher.other : rumors
-1.00  1.00 NullPunisher.other : threat
-3.00  1.00 NullPunisher.entity : British_Home_Office
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : specific
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : any
-1.00  1.00 NullPunisher.other : target
-0.05  1.00 NullPunisher.aux : had
-1.00  1.00 NullPunisher.other : denied
-2.00  1.00 RootEntailment.unalignedRoot : "denied" not aligned to anything
Hand-tuned score (dot product of above): -11.5040
Threshold: -1.8794


Inference ID: 2178

Txt: For women who are HIV negative or who do not know their HIV status, breastfeeding should be protected, promoted and supported for six months.

Hyp: For HIV-positive mothers, the decision about whether or not to breastfeed a child can be difficult. (don't know)

HIV-positive
JJ
mothers
NNS
the
DT
decision
NN
about
IN
whether
IN
not
RB
to
TO
breastfeed
VB
a
DT
child
NN
can
MD
be
VB
difficult
JJ
women:NNS 11.01   4.45 20.00   7.87 20.00 20.00 15.00 18.72 15.00 20.00   6.76 20.00 15.00 11.74
who:WP 15.00 12.00 20.00 12.00 20.00 20.00 20.00 20.00 15.00 20.00 12.00 18.57 15.00 15.00
are:VBP 12.00 15.00 20.00 15.00 20.00 20.00 20.00 20.00 10.00 20.00 15.00 20.00   0.00 12.00
HIV:RB 12.00 15.00 20.00 15.00 20.00 20.00 10.00 20.00 20.00 20.00 15.00 20.00 20.00 12.00
negative:JJ   3.70 12.00 20.00 11.58 20.00 20.00 12.00 20.00 12.00 20.00 12.00 20.00 12.00   7.83
who:WP 15.00 12.00 20.00 12.00 20.00 20.00 20.00 20.00 15.00 20.00 12.00 18.57 15.00 15.00
do:VBP 12.00 14.06 20.00 15.00 20.00 20.00 20.00 20.00 10.00 20.00 14.35 17.84 10.00 11.80
not:RB 12.00 15.00 20.00 15.00 20.00 20.00   0.00 20.00 20.00 20.00 15.00 20.00 20.00 12.00
know:VB 12.00 15.00 20.00 15.00 20.00 20.00 20.00 20.00 10.00 20.00 14.78 18.60 10.00 12.00
their:PRP$ 15.00 12.00 20.00 12.00 20.00 20.00 20.00 20.00 15.00 20.00 12.00 20.00 15.00 15.00
HIV:NN 12.00   9.70 20.00   9.56 20.00 20.00 15.00 20.00 15.00 20.00   9.60 20.00 15.00 12.00
status:NN 10.68   7.25 20.00   7.15 20.00 20.00 15.00 20.00 15.00 20.00   7.26 20.00 15.00   9.16
breastfeeding:NN 12.00   9.27 20.00   9.04 20.00 20.00 15.00 20.00 10.00 20.00   9.10 20.00 15.00 12.00
should:MD 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 10.00 20.00 10.00 20.00 20.00
be:VB 12.00 15.00 20.00 15.00 20.00 20.00 20.00 20.00 10.00 20.00 15.00 20.00   0.00 12.00
protected:VBN 12.00 15.00 20.00 12.19 20.00 20.00 20.00 20.00 10.00 20.00 14.72 20.00 10.00   9.91
promoted:VBN 12.00 14.25 20.00 15.00 20.00 20.00 20.00 20.00 10.00 20.00 15.00 20.00 10.00 12.00
supported:VBN 11.09 13.56 20.00 13.14 20.00 20.00 20.00 20.00 10.00 20.00 15.00 20.00 10.00 12.00
six:CD 19.73 20.36 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 19.83
months:NNS 10.12   7.75 20.00   7.31 20.00 20.00 15.00 20.00 15.00 20.00   7.42 20.00 15.00 12.00
NO_WORD   9.00 10.00   1.00 10.00 10.00 10.00   9.00 10.00 10.00   1.00 10.00 10.00 10.00   9.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.55 Alignment.score
 1.00  0.82 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  11.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.14 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added mothers[mothers-NNS]
 2.00  1.00 Modal.yes : actual -> possible
 0.00  1.00 NegPolarity.hypNegWord : "breastfeed": has child with relation "neg"
-1.00  1.00 NullPunisher.other : about
-0.10  1.00 NullPunisher.functionWord : whether
-1.00  1.00 NullPunisher.other : mothers
-1.00  1.00 NullPunisher.other : difficult
-0.05  1.00 NullPunisher.aux : can
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : child
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : HIV-positive
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : decision
-2.00  1.00 RootEntailment.unalignedRoot : "difficult" not aligned to anything
Hand-tuned score (dot product of above): -7.0634
Threshold: -1.8794


Inference ID: 2191

Txt: Saudi Arabia was boosting its production.

Hyp: Saudi Arabia was boosting its own production. (yes)

Saudi_Arabia
NNP
was
VBD
boosting
VBG
its
PRP$
own
JJ
production
NN
Saudi_Arabia:NNP   0.00 15.50 15.50 12.50 12.50 10.50
was:VBD 15.50   0.00 10.00 15.00 12.00 15.00
boosting:VBG 15.50 10.00   0.00 15.00 12.00 13.16
its:PRP$ 12.50 15.00 15.00   0.00 15.00 12.00
production:NN 10.50 15.00 13.16 12.00 12.00   0.00
NO_WORD 10.00   1.00 10.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.66 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.83 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added own[own-JJ]
-1.00  1.00 NullPunisher.other : own
Hand-tuned score (dot product of above): 0.9803
Threshold: -1.8794


Inference ID: 699

Txt: The city's motto is Contemnit procellas, which means, "It defies the storms."

Hyp: The city's motto is, appropriately, "Contemnit procellas" ( "It defies the storms"). (yes)

The
DT
city
NN
motto
NN
is
VBZ
appropriately
RB
Contemnit
NNP
procellas
NNS
It
PRP
defies
VBZ
the
DT
storms
NNS
The:DT   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00
city:NN 20.00   0.00   8.45 15.00 14.75 10.00 10.00 12.00 14.50 20.00   7.66
motto:NN 20.00   8.45   0.00 15.00 14.71 10.00 10.00 12.00 14.49 20.00   9.29
is:VBZ 20.00 15.00 15.00   0.00 20.00 15.00 15.00 15.00 10.00 20.00 15.00
Contemnit:NNP 20.00 10.00 10.00 15.00 15.00   0.00 10.00 12.00 15.00 20.00 10.00
procellas:NNS 20.00 10.00 10.00 15.00 15.00 10.00   0.00 12.00 15.00 20.00 10.00
which:WDT 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00
means:VBZ 20.00 15.00 15.00 10.00 20.00 15.00 15.00 15.00   7.79 20.00 15.00
It:PRP 20.00 12.00 12.00 15.00 20.00 12.00 12.00   0.00 15.00 20.00 12.00
defies:VBZ 20.00 14.50 14.49 10.00 18.57 15.00 15.00 15.00   0.00 20.00 14.89
the:DT   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00
storms:NNS 20.00   7.66   9.29 15.00 13.10 10.00 10.00 12.00 14.89 20.00   0.00
NO_WORD   1.00 10.00 10.00 10.00   9.00 10.00 10.00 10.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.56 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.45 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added appropriately[appropriately-RB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "means" of "procellas" dropped on aligned hyp word "procellas"
-1.00  1.00 NullPunisher.other : appropriately
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "is" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.1127
Threshold: -1.8794


Inference ID: 2170

Txt: Hacking reported his wife missing on July 19, a Monday.

Hyp: Mark Hacking was booked into the Salt Lake County Jail on Monday. (don't know)

Mark
NNP
Hacking
NNP
was
VBD
booked
VBN
the
DT
Salt_Lake_County_Jail
NNP
Monday
NNP
Hacking:NNP   9.43   0.00 15.00 15.00 20.00 10.07   9.76
reported:VBD 15.00 15.00 10.00   7.85 20.00 15.50 15.50
his:PRP$ 12.00 12.00 15.00 15.00 20.00 12.50 12.50
wife:NN   8.64   9.32 15.00 14.52 20.00   9.05   8.87
missing:VBG 15.00 15.00 10.00   8.85 20.00 15.50 15.50
July:NNP   9.16   9.83 15.50 15.50 20.50   9.41   6.04
19:CD 20.50 20.50 20.50 20.07 20.50 20.50 20.00
a:DT 20.00 20.00 20.00 20.00 10.00 20.50 20.50
Monday:NNP   9.06   9.76 15.50 15.50 20.50   9.32   0.00
NO_WORD 10.00 10.00   1.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.80 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.14 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Salt_Lake_County_Jail[Salt_Lake_County_Jail-NNP]
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1000
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : Mark
-1.00  1.00 NullPunisher.other : booked
-0.05  1.00 NullPunisher.aux : was
-3.00  1.00 NullPunisher.entity : Salt_Lake_County_Jail
-2.00  1.00 RootEntailment.unalignedRoot : "booked" not aligned to anything
Hand-tuned score (dot product of above): -6.3749
Threshold: -1.8794


Inference ID: 2164

Txt: With $549 million in cash as of June 30, Google can easily afford to make amends.

Hyp: Some 30 million shares have been assigned to the company's workers. (don't know)

Some
DT
30
CD
million
CD
shares
NNS
have
VBP
been
VBN
assigned
VBN
the
DT
company
NN
workers
NNS
$:$ 10.50 20.50 17.13 19.89 20.50 20.50 20.50 10.50 19.85 20.50
549:CD 20.50   7.95   7.64 17.59 20.50 20.50 20.50 20.50 20.50 20.48
million:CD 20.50   8.64   0.50 16.86 20.50 20.50 19.01 20.50 17.54 20.50
cash:NN 20.00 20.50 18.50   5.55 15.00 15.00 14.93 20.00   6.78   6.60
June:NNP 20.50 20.50 20.50   7.37 15.50 15.50 15.50 20.50   7.98   7.72
30:CD 20.50   0.50   8.64 20.50 20.50 20.50 20.50 20.50 19.02 20.22
Google:NNP 20.50 20.50 20.50 10.50 15.50 15.50 15.50 20.50 10.50 10.50
can:MD 10.00 20.50 20.50 20.00 18.11 20.00 20.00 10.00 20.00 20.00
easily:RB 20.00 20.43 20.50 15.00 20.00 20.00 19.09 20.00 13.80 15.00
afford:VB 20.00 20.50 20.50 15.00   7.90 10.00   9.49 20.00 15.00 13.06
to:TO 10.00 20.50 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00
make:VB 20.00 19.84 20.48 15.00   6.34 10.00   9.86 20.00 14.65 14.32
amends:NNS 20.00 20.50 20.50   7.79 15.00 15.00 15.00 20.00   7.61   7.35
NO_WORD 10.00 10.00 10.00 10.00   1.00   1.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.46 Alignment.score
 1.00  0.81 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  8.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.10 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added workers[workers-NNS]
-0.05  1.00 NullPunisher.aux : been
-1.00  1.00 NullPunisher.other : shares
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : Some
-0.05  1.00 NullPunisher.aux : have
-1.00  1.00 NullPunisher.other : company
-1.00  1.00 NullPunisher.other : workers
-1.00  1.00 NullPunisher.other : assigned
-2.00  1.00 RootEntailment.unalignedRoot : "assigned" not aligned to anything
Hand-tuned score (dot product of above): -7.5185
Threshold: -1.8794


Inference ID: 750

Txt: At the southeastern tip of the city, Xochimilco, another small town subsumed by the city, is a popular tourist destination because of its chinampas, or "floating gardens," boats made out of reeds on which the Indians have grown plants since pre-Columbian times.

Hyp: Xochimilco is a popular tourist attraction because of its chinampas, or floating gardens. (yes)

Xochimilco
NNP
is
VBZ
a
DT
popular
JJ
tourist
NN
attraction
NN
its
PRP$
chinampas
NNS
floating
JJ
gardens
NNS
the:DT 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
southeastern:JJ 12.00 12.00 20.00   9.55 10.43   9.09 15.00 12.00   9.11 10.67
tip:NN 10.00 15.00 20.00 12.00   6.76   8.71 12.00 10.00   8.83   7.62
the:DT 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
city:NN 10.00 15.00 20.00 11.66   5.03   6.76 12.00 10.00 12.00   4.40
Xochimilco:NNP   0.50 15.50 20.50 12.50 10.50 10.50 12.50 10.50 12.50 10.50
another:DT 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
small_town:NN 10.00 15.00 20.00 12.00   8.99   8.08 12.00 10.00 12.00   8.85
subsumed:VBN 15.00 10.00 20.00 12.00 15.00 14.85 15.00 15.00 11.12 15.00
the:DT 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
city:NN 10.00 15.00 20.00 11.66   5.03   6.76 12.00 10.00 12.00   4.40
is:VBZ 15.00   0.00 20.00 12.00 15.00 15.00 13.00 15.00 12.00 15.00
a:DT 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
popular:JJ 12.00 12.00 20.00   0.00 11.61   9.14 15.00 12.00   9.57 12.00
tourist:NN 10.00 15.00 20.00 11.61   0.00   5.06 12.00 10.00 10.86   4.88
destination:NN 10.00 15.00 20.00 10.18   3.23   5.40 12.00 10.00 10.15   6.01
its:PRP$ 12.00 13.00 20.00 15.00 12.00 12.00   0.00 12.00 15.00 12.00
chinampas:NNS 10.00 15.00 20.00 12.00 10.00 10.00 12.00   0.00 12.00 10.00
floating:JJ 12.00 12.00 20.00   9.57 10.86   9.46 15.00 12.00   0.00   9.67
gardens:NNS 10.00 15.00 20.00 12.00   4.88   5.82 12.00 10.00   9.67   0.00
boats:NNS 10.00 15.00 20.00 12.00   7.08   7.68 12.00 10.00   9.55   6.29
made_out:VBN 15.00 10.00 20.00 12.00 15.00 15.00 15.00 15.00 12.00 15.00
reeds:NNS 10.00 15.00 20.00 10.87   8.53   8.91 12.00 10.00 10.32   9.20
on:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
Indians:NNPS 10.00 15.00 20.00 12.00   7.84   7.76 12.00 10.00 12.00   8.21
have:VBP 15.00 10.00 20.00 12.00 15.00 15.00 15.00 15.00 12.00 15.00
grown:VBN 15.00 10.00 20.00 10.90 14.31 14.15 15.00 15.00 12.00 15.00
plants:NNS 10.00 15.00 20.00 12.00   8.12   7.57 12.00 10.00 10.14   8.05
pre-Columbian:JJ 12.00 12.00 20.00 10.00 12.00 12.00 15.00 12.00 10.00 12.00
times:NNS 10.00 15.00 20.00 12.00   8.85   7.89 12.00 10.00 12.00   8.71
NO_WORD 10.00   1.00   1.00   9.00 10.00 10.00 10.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.76 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  10.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "tip" of "destination" dropped on aligned hyp word "attraction"
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "attraction" aligned badly to "destination"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.7487
Threshold: -1.8794


Inference ID: 2165

Txt: Google Inc. is delaying its IPO by a week because of logistical problems related to institutional investors registering to bid on the shares.

Hyp: A delay for Google's IPO would push the deal later into a month that is traditionally light on IPO volume. (don't know)

A
DT
delay
NN
Google
NNP
IPO
NNP
would
MD
push
VB
the
DT
deal
NN
later
RB
a
DT
month
NN
that
WDT
is
VBZ
traditionally
RB
light
JJ
IPO
NNP
volume
NN
Google_Inc.:NNP 20.50   8.57   5.50   8.80 20.50 15.50 20.50   7.89 15.50 20.50   7.10 20.50 15.50 15.50 12.50   8.80   8.14
is:VBZ 20.00 15.00 15.50 15.50 20.00 10.00 20.00 15.00 20.00 20.00 15.00 20.00   0.00 20.00 12.00 15.50 15.00
delaying:VBG 20.00   0.00 15.50 15.50 20.00   6.99 20.00 12.99 16.06 20.00 13.99 20.00 10.00 19.80 10.75 15.50 15.00
its:PRP$ 20.00 12.00 12.50 12.50 20.00 15.00 20.00 12.00 20.00 20.00 12.00 20.00 13.00 20.00 15.00 12.50 12.00
IPO:NN 20.00   9.34 10.50   0.50 20.00 15.00 20.00   6.07 15.00 20.00   8.49 20.00 15.00 15.00 12.00   0.50   9.11
a:DT   0.00 20.00 20.50 20.50 10.00 20.00 10.00 20.00 20.00   0.00 20.00 10.00 20.00 20.00 20.00 20.50 20.00
week:NN 20.00   6.56 10.50   9.39 20.00 14.86 20.00   7.53 13.49 20.00   3.49 20.00 15.00 15.00   8.96   9.39   5.96
logistical:JJ 20.00 11.51 12.50 12.50 20.00 11.43 20.00 11.02 12.00 20.00 12.00 20.00 12.00 11.09   8.91 12.50 10.69
problems:NNS 20.00   7.82 10.50   9.45 20.00 13.70 20.00   7.63 15.00 20.00   6.88 20.00 15.00 13.70 11.46   9.45   7.87
related:VBN 20.00 14.05 15.50 15.50 20.00 10.00 20.00 14.96 15.00 20.00 15.00 20.00 10.00 20.00 12.00 15.50 14.52
institutional:JJ 20.00 12.00 12.50 12.50 20.00 12.00 20.00 12.00 12.00 20.00 11.49 20.00 12.00   8.64   9.05 12.50 11.59
investors:NNS 20.00   8.06 10.50   9.29 20.00 12.47 20.00   7.37 13.21 20.00   6.58 20.00 15.00 13.65 11.37   9.29   7.62
registering:VBG 20.00 15.00 15.50 15.50 20.00   9.32 20.00 15.00 18.82 20.00 14.15 20.00 10.00 15.45 11.85 15.50 12.10
to:TO 10.00 20.00 20.50 20.50 10.00 20.00 10.00 20.00 20.00 10.00 20.00 10.00 20.00 20.00 20.00 20.50 20.00
bid:VB 20.00 13.70 15.50 15.50 20.00   8.33 20.00 10.44 20.00 20.00 14.08 20.00 10.00 20.00 10.87 15.50 15.00
the:DT 10.00 20.00 20.50 20.50 10.00 20.00   0.00 20.00 20.00 10.00 20.00 10.00 20.00 20.00 20.00 20.50 20.00
shares:NNS 20.00   7.07 10.50   9.12 20.00 15.00 20.00   7.10 15.00 20.00   5.22 20.00 15.00 15.00 11.37   9.12   4.00
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00   9.00   1.00 10.00   1.00   1.00   9.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.51 Alignment.score
 1.00  0.82 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  12.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.24 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added volume[volume-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "problems" of "week" dropped on aligned hyp word "month"
-0.10  1.00 NullPunisher.functionWord : that
-0.05  1.00 NullPunisher.aux : would
-0.10  1.00 NullPunisher.article : A
-1.00  1.00 NullPunisher.other : deal
-3.00  1.00 NullPunisher.entity : IPO
-1.00  1.00 NullPunisher.other : push
-1.00  1.00 NullPunisher.other : traditionally
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : volume
-3.00  1.00 NullPunisher.entity : Google
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : light
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "push" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -11.9474
Threshold: -1.8794


Inference ID: 2160

Txt: Prosecutors were still expected to unseal an 11-count manslaughter indictment against the ferry's captain, Michael Gansas.

Hyp: Gansas' attorneys have said that rule was not communicated to ferry staff. (don't know)

Gansas
NNP
attorneys
NNS
have
VBP
said
VBD
that
IN
rule
NN
was
VBD
not
RB
communicated
VBN
to
TO
ferry
VB
staff
NN
Prosecutors:NNS 10.50   1.50 15.00 15.00 20.00   8.55 15.00 15.00 15.00 20.00 15.00   8.46
were:VBD 15.50 15.00 10.00 10.00 20.00 15.00   0.00 20.00 10.00 20.00 10.00 15.00
still:RB 15.50 15.00 20.00 20.00 20.00 15.00 20.00 10.00 20.00 20.00 20.00 15.00
expected:VBN 15.50 14.94 10.00   7.98 20.00 13.91 10.00 20.00 10.00 20.00   9.94 14.16
to:TO 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00
unseal:VB 15.50   8.67 10.00 10.00 20.00 12.04 10.00 20.00   9.45 20.00 10.00 15.00
an:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00   8.20 20.00 20.00
11-count:JJ 12.50 11.63 12.00 12.00 20.00 10.09 12.00 12.00 11.68 20.00 12.00 11.59
manslaughter:NN 10.50   4.68 15.00 14.21 20.00   7.97 15.00 15.00 15.00 20.00 15.00 10.00
indictment:NN 10.50   4.97 15.00 15.00 20.00   8.16 15.00 15.00 13.51 20.00 15.00   8.73
the:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00
ferry:NN 10.50   8.68 15.00 14.57 20.00   8.72 15.00 15.00 15.00 20.00   0.00   8.76
captain:NN 10.50   8.29 15.00 15.00 20.00   9.26 15.00 15.00 13.80 20.00 14.34   7.00
Michael_Gansas:NNP   5.50   8.75 15.50 15.50 20.50   7.11 15.50 15.50 15.50 20.50 15.50   8.80
NO_WORD 10.00 10.00   1.00 10.00   1.00 10.00   1.00   9.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.46 Alignment.score
 1.00  0.81 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  9.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "still" of "expected" dropped on aligned hyp word "communicated"
 0.00  1.00 NegPolarity.hypNegWord : "communicated": has child with relation "neg"
-1.00  1.00 NullPunisher.other : said
-1.00  1.00 NullPunisher.other : staff
-0.10  1.00 NullPunisher.functionWord : that
-1.00  1.00 NullPunisher.other : not
-1.00  1.00 NullPunisher.other : attorneys
-3.00  1.00 NullPunisher.entity : Gansas
-0.10  1.00 NullPunisher.functionWord : to
-0.05  1.00 NullPunisher.aux : have
-1.00  1.00 NullPunisher.other : rule
-2.00  1.00 RootEntailment.unalignedRoot : "said" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -8.7634
Threshold: -1.8794


Inference ID: 1953

Txt: The latest attacks targeted the U-S embassy and a top prosecutor's office in the Uzbek capital.

Hyp: Yesterday's terrorist bombings killed two people at the entrance to the Israeli Embassy in Uzbekistan's capital. (don't know)

Yesterday
NN
terrorist
JJ
bombings
NNS
killed
VBD
two
CD
people
NNS
the
DT
entrance
NN
the
DT
Israeli
NNP
Embassy
NNP
Uzbekistan
NNP
capital
NN
The:DT 20.00 20.00 20.00 20.00 20.50 20.00   0.00 20.00   0.00 20.00 20.00 20.50 20.00
latest:JJS 12.00   9.92 11.62 12.00 19.75 12.00 20.00 12.00 20.00 12.00 12.00 12.50 11.69
attacks:NNS   7.70   6.11   2.89 10.33 19.95   6.61 20.00   8.26 20.00   9.02   8.95   9.94   7.26
targeted:VBD 15.00 11.22 14.94   7.95 19.66 14.76 20.00 14.50 20.00 15.00 15.00 15.50 14.15
the:DT 20.00 20.00 20.00 20.00 20.50 20.00   0.00 20.00   0.00 20.00 20.00 20.50 20.00
U-S:JJ 12.00 10.00 12.00 12.00 20.50 12.00 20.00 12.00 20.00 12.00 12.00 12.50 12.00
embassy:NN   8.92   7.09   7.73 12.35 20.50   8.20 20.00   8.31 20.00   9.18   0.00   9.93   8.64
a:DT 20.00 20.00 20.00 20.00 20.50 20.00 10.00 20.00 10.00 20.00 20.00 20.50 20.00
top:JJ 12.00 10.00 12.00 10.71 18.82 12.00 20.00 10.73 20.00 12.00 12.00 12.50 10.89
prosecutor:NN   8.40   9.40   8.50 12.75 20.50   7.52 20.00   8.38 20.00   8.41   9.04   9.87   8.05
office:NN   7.75 10.78   9.20 14.44 20.35   6.68 20.00   7.19 20.00   8.63   6.41   9.73   7.32
the:DT 20.00 20.00 20.00 20.00 20.50 20.00   0.00 20.00   0.00 20.00 20.00 20.50 20.00
Uzbek:NNP 10.50 12.50 10.50 15.50 20.50 10.50 20.50 10.50 20.50 10.50 10.50   3.00 10.50
capital:NN   7.21 10.73   8.90 15.00 20.50   6.00 20.00   7.84 20.00   8.72   8.64   9.72   0.00
NO_WORD 10.00   9.00 10.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.57 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.46 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added entrance[entrance-NN]
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: attack -> bombing
 0.00  1.00 NegPolarity.txtNegWord : "latest": tag "JJS" is in NegPolarityMarkers list
-3.00  1.00 NullPunisher.entity : two
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : entrance
-1.00  1.00 NullPunisher.other : Yesterday
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "killed" aligned badly to "targeted"
-1.00  1.00 Structure.relMismatch : text "embassy" is dobj of "targeted" while hyp "Embassy" is prep_to of "killed" which aligned to text "targeted"
Hand-tuned score (dot product of above): -8.5562
Threshold: -1.8794


Inference ID: 2116

Txt: Abu Musa al-Hindi was arrested following a tip-off from Pakistani Intelligence who claimed that he was receiving direct orders from Osama bin Laden.

Hyp: The possibility of an attack began with the arrest of Naeem Noor Khan. (don't know)

The
DT
possibility
NN
an
DT
attack
NN
began
VBD
the
DT
arrest
NN
Naeem_Noor_Khan
NNP
Abu_Musa_al-Hindi:NNP 20.50 10.50 20.50 10.50 15.50 20.50 10.50 10.00
was:VBD 20.00 15.00 20.00 15.00 10.00 20.00 15.00 15.50
arrested:VBN 20.00 14.29 20.00 10.60   7.74 20.00   0.00 15.50
following:VBG 20.00 13.64 20.00 14.74   8.48 20.00 13.94 15.50
a:DT 10.00 20.00   8.73 20.00 20.00 10.00 20.00 20.50
tip-off:NN 20.00   8.25 20.00   9.32 15.00 20.00   9.73 10.38
Pakistani_Intelligence:NNP 20.50 10.26 20.50   9.94 15.50 20.50 10.29   9.23
who:WP 20.00 12.00 20.00 12.00 15.00 20.00 12.00 12.50
claimed:VBD 20.00 12.47 20.00 10.42   6.81 20.00 10.06 15.50
that:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50
he:PRP 18.00 12.00 20.00 12.00 15.00 18.00 12.00 12.50
was:VBD 20.00 15.00 20.00 15.00 10.00 20.00 15.00 15.50
receiving:VBG 20.00 14.19 20.00 14.09   7.49 20.00 13.29 15.50
direct:JJ 20.00 12.00 20.00 10.36 11.39 20.00 12.00 12.50
orders:NNS 20.00   8.63 20.00   6.78 14.93 20.00   7.95   9.66
Osama_bin_Laden:NNP 20.50 10.50 20.50 10.50 15.50 20.50 10.50 10.00
NO_WORD   1.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.36 Alignment.score
 1.00  0.80 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  7.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.12 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Naeem_Noor_Khan[Naeem_Noor_Khan-NNP]
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : The
-1.00  1.00 NullPunisher.other : attack
-3.00  1.00 NullPunisher.entity : Naeem_Noor_Khan
-1.00  1.00 NullPunisher.other : began
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : possibility
-2.00  1.00 RootEntailment.unalignedRoot : "began" not aligned to anything
Hand-tuned score (dot product of above): -8.7299
Threshold: -1.8794


Inference ID: 735

Txt: Even more than other economic activities, Mexico's financial services are concentrated in the capital.

Hyp: Industry, retail stores, finance, and communications are all centered in the capital. (yes)

Industry
NNP
retail_stores
NNS
finance
NN
communications
NNS
are
VBP
all
RB
centered
VBN
the
DT
capital
NN
Even:RB 15.00 15.00 15.00 15.00 20.00 10.00 20.00 20.00 15.00
more:RBR 15.00 15.00 15.00 15.00 20.00 10.00 20.00 20.00 15.00
other:JJ 12.00 12.00 12.00 12.00 12.00 12.00 12.00 20.00 12.00
economic:JJ 12.00 12.00   9.94 12.00 12.00 12.00 10.78 20.00   9.58
activities:NNS   4.83   4.85   3.72   6.57 15.00 15.00 13.52 20.00   5.12
Mexico:NNP   8.39   7.91   8.70   9.36 15.50 15.50 15.50 20.50   8.56
financial:JJ 12.00 12.00   7.00 11.94 12.00 12.00 12.00 20.00   8.31
services:NNS   7.15   7.17   6.37   4.62 15.00 15.00 14.91 20.00   7.35
are:VBP 15.00 15.00 15.00 15.00   0.00 20.00 10.00 20.00 15.00
concentrated:VBN 15.00 15.00 15.00 15.00 10.00 20.00   5.52 20.00 14.14
the:DT 20.00 20.00 20.00 18.72 20.00 20.00 20.00   0.00 20.00
capital:NN   6.49   6.51   6.91   7.85 15.00 15.00 15.00 20.00   0.00
NO_WORD 10.00 10.00 10.00 10.00   1.00   9.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.49 Alignment.score
 1.00  0.82 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.22 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added all[all-RB]
-1.00  1.00 NullPunisher.other : all
-1.00  1.00 NullPunisher.other : communications
-1.00  1.00 NullPunisher.other : retail_stores
-1.00  1.00 NullPunisher.other : Industry
-0.05  1.00 NullPunisher.aux : are
-1.00  1.00 NullPunisher.other : centered
-2.00  1.00 RootEntailment.unalignedRoot : "centered" not aligned to anything
Hand-tuned score (dot product of above): -7.1215
Threshold: -1.8794


Inference ID: 2172

Txt: Benjamin Vanderford, 22, said he posted the 55-second clip, which shows a knife sawing against his neck, on an online file-sharing network in May.

Hyp: Mr Vanderford said he filmed his own mock execution in a friend's garage to show how the media could be fooled. (don't know)

Mr_Vanderford
NNP
said
VBD
he
PRP
filmed
VBN
his
PRP$
own
JJ
mock
JJ
execution
NN
a
DT
friend
NN
garage
NN
to
TO
show
VB
how
WRB
the
DT
media
NNS
could
MD
be
VB
fooled
VBN
Benjamin_Vanderford:NNP   5.00 15.50 12.50 15.50 12.50 12.50 12.50   9.99 20.50   9.10   9.87 20.50 15.50 20.50 20.50   8.77 20.50 15.50 15.50
22:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.08 20.23 20.50 20.50 20.50 20.50 19.90 20.50 20.50 20.50 20.50 20.50 20.50
said:VBD 15.50   0.00 15.00 10.00 15.00 12.00 12.00 14.24 20.00 15.00 15.00 20.00   9.93 20.00 20.00 14.96 20.00 10.00   9.46
he:PRP 12.50 15.00   0.00 15.00 10.00 15.00 15.00 12.00 20.00 12.00 12.00 20.00 15.00 20.00 18.00 12.00 20.00 15.00 15.00
posted:VBD 15.50   9.86 15.00 10.00 15.00 12.00 12.00 15.00 20.00 15.00 14.27 20.00   9.80 20.00 20.00 14.69 20.00 10.00 10.00
the:DT 20.50 20.00 18.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 10.00   0.00 20.00 10.00 20.00 20.00
55-second:JJ 12.50 12.00 15.00 11.61 15.00 10.00 10.00 11.88 20.00 12.00 12.00 20.00 11.68 20.00 20.00 12.00 20.00 12.00 11.28
clip:NN   7.17 15.00 12.00 11.29 12.00 12.00 10.82   8.90 20.00   7.46   8.31 20.00 11.06 20.00 20.00   5.67 20.00 15.00 14.39
which:WDT 20.50 20.00 17.85 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 10.00 10.00 20.00 10.00 20.00 20.00
shows:VBZ 15.50 10.00 15.00   4.10 15.00 12.00   9.76 15.00 20.00 15.00 15.00 20.00   0.00 20.00 20.00   9.94 20.00 10.00   9.05
a:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 10.00 20.00 10.00 10.00 20.00 10.00 20.00 20.00
knife:NN   8.54 14.46 12.00 14.85 12.00 12.00   9.36   8.36 20.00   6.10   6.25 20.00 15.00 20.00 20.00   7.21 20.00 15.00 15.00
sawing:NN   9.14 14.36 12.00 14.44 12.00 12.00   9.75   9.58 20.00   9.08   7.72 20.00 14.35 20.00 20.00   8.82 20.00 15.00 12.49
his:PRP$ 12.50 15.00 10.00 15.00   0.00 15.00 15.00 12.00 20.00 12.00 12.00 20.00 15.00 20.00 20.00 12.00 20.00 15.00 15.00
neck:NN   8.37 15.00 12.00 15.00 12.00 12.00 10.47   8.29 20.00   7.16   7.12 20.00 15.00 20.00 20.00   8.11 20.00 15.00 15.00
an:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00   8.73 20.00 20.00   8.20 20.00 10.00 10.00 20.00 10.00 20.00 20.00
online:JJ 12.50 11.60 15.00 12.00 15.00 10.00 10.00 11.29 20.00 12.00 12.00 20.00 11.94 20.00 20.00   9.61 20.00 12.00 12.00
file-sharing:JJ 12.50 11.92 15.00 12.00 15.00 10.00 10.00 11.91 20.00 11.63 10.78 20.00 11.86 20.00 20.00   9.85 20.00 12.00 12.00
network:NN   7.92 15.00 12.00 12.07 12.00 12.00 10.59   8.91 20.00   8.10   9.28 20.00 11.90 20.00 20.00   3.95 20.00 15.00 15.00
May:NNP   8.22 15.50 12.50 15.50 12.50 12.50 12.50   9.58 20.50   8.84   9.92 20.50 15.50 20.50 20.50   8.47 20.50 15.50 15.50
NO_WORD 10.00 10.00 10.00 10.00 10.00   9.00   9.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.52 Alignment.score
 1.00  0.82 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  12.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.26 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added how[how-WRB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "network" of "shows" dropped on aligned hyp word "show"
-1.00  1.00 NullPunisher.other : fooled
-1.00  1.00 NullPunisher.other : how
-0.05  1.00 NullPunisher.aux : be
-0.10  1.00 NullPunisher.functionWord : his
-0.05  1.00 NullPunisher.aux : could
-1.00  1.00 NullPunisher.other : media
-0.10  1.00 NullPunisher.functionWord : to
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : garage
-1.00  1.00 NullPunisher.other : friend
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : own
-2.00  1.00 Person.mismatch : person mimatch between Mr_Vanderford and Benjamin_Vanderford
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -7.9400
Threshold: -1.8794


Inference ID: 802

Txt: Reagan was seriously wounded by a bullet fired by John Hinckley Jr. in an attempted assassination on March 30, 1981.

Hyp: Reagan almost died on March 30, 1981, when would-be assassin John W. Hinckley Jr. shot him. (yes)

Reagan
NNP
almost
RB
died
VBD
March
NNP
30
CD
1981
CD
when
WRB
would-be
JJ
assassin
NN
John_W._Hinckley_Jr.
NNP
shot
VBD
him
PRP
Reagan:NNP   0.00 15.50 15.50   9.29 20.50 20.50 20.50 12.50   8.89   7.97 15.50 12.50
was:VBD 15.50 20.00 10.00 15.50 20.50 20.50 20.00   2.00 15.00 15.50 10.00 15.00
seriously:RB 15.50 10.00 16.61 15.50 19.71 20.50 20.00 12.00 14.99 15.50 18.93 15.00
wounded:VBN 15.50 20.00   4.94 15.50 20.50 20.50 20.00 12.00 11.43 15.50   5.52 15.00
a:DT 20.50 20.00 20.00 20.50 20.50 20.50 10.00 20.00 20.00 20.50 20.00 20.00
bullet:NN   9.54 15.00 11.96   9.75 20.44 20.37 20.00 12.00   7.13   8.70   7.94 12.00
fired:VBN 15.50 20.00   8.52 15.50 20.50 19.56 20.00 12.00 13.41 15.50   6.30 15.00
John_Hinckley_Jr.:NNP   7.97 15.50 15.50   8.81 20.50 20.50 20.50 12.50   9.12   0.00 15.50 12.50
an:DT 20.50 20.00 20.00 20.50 20.50 20.50 10.00 20.00 20.00 20.50 20.00 20.00
attempted:VBN 15.50 20.00   6.68 15.50 20.50 18.10 20.00 12.00 11.42 15.50   4.95 15.00
assassination:NN   9.74 15.00 11.98   9.13 20.16 19.26 20.00 12.00   1.00   9.37 13.68 12.00
March:NNP   9.29 15.50 15.50   0.00 20.00 20.00 20.50 12.50   9.75   8.81 15.50 12.50
30:CD 20.50 20.50 19.29 20.00   0.00   0.00 20.50 20.50 20.39 20.50 20.16 20.50
1981:CD 20.50 20.50 17.91 20.00   0.00   0.00 20.50 20.50 18.96 20.50 17.83 20.50
NO_WORD 10.00   9.00 10.00 10.00 10.00 10.00 10.00   9.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.87 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added would-be[would-be-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "attempted" of "assassination" dropped on aligned hyp word "assassin"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 03/30/1981
-1.00  1.00 NullPunisher.other : him
-1.00  1.00 NullPunisher.other : would-be
-1.00  1.00 NullPunisher.other : died
-1.00  1.00 NullPunisher.other : when
-1.00  1.00 NullPunisher.other : almost
-2.00  1.00 Person.mismatch : person mimatch between John_W._Hinckley_Jr. and John_Hinckley_Jr.
-2.00  1.00 RootEntailment.unalignedRoot : "died" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -6.9281
Threshold: -1.8794


Inference ID: 827

Txt: Jennifer Hawkins is the 21-year-old beauty queen from Australia.

Hyp: Jennifer Hawkins is Australia's 21-year-old beauty queen. (yes)

Jennifer_Hawkins
NNP
is
VBZ
Australia
NNP
21-year-old
JJ
beauty
NN
queen
NN
Jennifer_Hawkins:NNP   0.00 15.50   9.84 12.50 10.00   9.23
is:VBZ 15.50   0.00 15.50 12.00 15.00 15.00
the:DT 20.50 20.00 20.50 20.00 20.00 20.00
21-year-old:JJ 12.50 12.00 12.50   0.00 11.75 12.00
beauty:NN 10.00 15.00   9.34 11.75   0.00   4.23
queen:NN   9.23 15.00   9.73 12.00   4.23   0.00
Australia:NNP   9.84 15.50   0.00 12.50   9.34   9.73
NO_WORD 10.00   1.00 10.00   9.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.20 Alignment.score
 1.00  0.96 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
-1.00  1.00 Structure.relMismatch : text "Australia" is prep_from of "queen" while hyp "Australia" is poss of "queen" which aligned to text "queen"
Hand-tuned score (dot product of above): 2.8606
Threshold: -1.8794


Inference ID: 1599

Txt: The board of Marks & Spencer will take another look at Philip Green's increased takeover offer.

Hyp: Philip Green tries to take over Marks & Spencer. (yes)

Philip_Green
NNP
tries
VBZ
to
TO
take
VB
Marks_&_Spencer
NNP
The:DT 20.50 20.00 10.00 20.00 20.50
board:NN   8.99 15.00 20.00 13.56   8.81
Marks_&_Spencer:NNP   8.06 15.50 20.50 15.50   0.00
will:MD 20.50 20.00 10.00 20.00 20.50
take:VB 15.50   7.31 20.00   0.00 15.50
another:DT 20.50 20.00 10.00 20.00 20.50
look:NN   9.24 14.21 20.00 13.26   9.07
Philip_Green:NNP   0.50 15.50 20.50 15.50   8.56
increased:VBN 15.50 10.00 20.00 10.00 15.50
takeover:NN   9.15 14.06 20.00   2.50   8.99
offer:NN   8.99 14.29 20.00 14.58   8.81
NO_WORD 10.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.08 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : tries
-2.00  1.00 RootEntailment.unalignedRoot : "tries" not aligned to anything
Hand-tuned score (dot product of above): -1.1142
Threshold: -1.8794


Inference ID: 1604

Txt: A brother of Colombia's education minister has been killed by Marxist rebels who kidnapped him three years ago.

Hyp: Colombia's education minister has been kidnapped by Marxist rebels. (don't know)

Colombia
NNP
education
NN
minister
NN
has
VBZ
been
VBN
kidnapped
VBN
Marxist
JJ
rebels
NNS
A:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00
brother:NN   9.47   8.40   7.26 15.00 15.00   8.73 12.00   7.66
Colombia:NNP   0.00   9.50   9.49 15.50 15.50 15.50 12.50   9.78
education:NN   9.50   0.00   8.42 15.00 15.00 15.00 12.00   8.81
minister:NN   9.49   8.42   0.00 15.00 15.00 12.11 12.00   4.40
has:VBZ 15.50 15.00 15.00   0.00 10.00 10.00 12.00 15.00
been:VBN 15.50 15.00 15.00 10.00   0.00 10.00 12.00 15.00
killed:VBN 15.50 15.00 14.57 10.00 10.00   2.47 12.00 10.45
Marxist:JJ 12.50 12.00 12.00 12.00 12.00 12.00   0.00 12.00
rebels:NNS   9.78   8.81   4.40 15.00 15.00 10.71 12.00   0.00
who:WP 12.50 12.00 12.00 15.00 15.00 15.00 15.00 12.00
kidnapped:VBD 15.50 15.00 12.11 10.00 10.00   0.00 12.00 10.71
him:PRP 12.50 12.00 12.00 15.00 15.00 15.00 15.00 12.00
three:CD 20.50 20.50 20.50 20.50 20.50 19.35 20.50 20.50
years:NNS   9.23   7.61   8.06 15.00 15.00 14.03 12.00   8.50
ago:RB 15.50 15.00 14.71 20.00 20.00 18.66 12.00 15.00
NO_WORD 10.00 10.00 10.00   1.00   1.00 10.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.02 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.38 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "ago" of "kidnapped" dropped on aligned hyp word "kidnapped"
-0.05  1.00 NullPunisher.aux : has
-0.05  1.00 NullPunisher.aux : been
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "minister" <-prep_of-- "brother" vs. hyp "minister" <-nsubjpass-- "kidnapped", which aligned to text "kidnapped"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.9866
Threshold: -1.8794


Inference ID: 1614

Txt: CBS newsman Harry Reasoner is returning to his Iowa hometown to get married Saturday.

Hyp: CBS newsman Harry Reasoner was born in Iowa. (yes)

CBS
NNP
newsman
NNP
Harry_Reasoner
NNP
was
VBD
born
VBN
Iowa
NNP
CBS:NNP   0.00 10.50 10.50 15.50 15.50 10.50
newsman:NNP 10.50   0.00   9.04 15.00 14.00   8.85
Harry_Reasoner:NNP 10.50   9.04   0.00 15.50 15.50   9.12
is:VBZ 15.50 15.00 15.50   0.00 10.00 15.50
returning:VBG 15.50 13.71 15.50 10.00   5.94 15.50
his:PRP$ 12.50 12.00 12.50 15.00 15.00 12.50
Iowa:NNP 10.50   8.85   9.12 15.50 15.50   0.00
hometown:NN 10.50   8.17   9.95 15.00 10.68   9.87
to:TO 20.50 20.00 20.50 20.00 20.00 20.50
get:VB 15.50 14.99 15.50 10.00 10.00 15.50
married:JJ 12.50 11.41 12.50 12.00   5.27 12.50
Saturday:NNP 10.50   9.61 10.19 15.50 15.50   9.86
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.88 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : born
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 RootEntailment.unalignedRoot : "born" not aligned to anything
Hand-tuned score (dot product of above): -0.0743
Threshold: -1.8794


Inference ID: 2007

Txt: The program, financed with a grant from the UN Agency for International Development, is aimed at reducing infant mortality in Madagascar, an island nation in the Indian Ocean off the eastern coast of Africa.

Hyp: The UN Agency for International Development is an organisation based in Madagascar. (don't know)

The
DT
UN_Agency_for_International_Development
NNP
is
VBZ
an
DT
organization
NN
based
VBN
Madagascar
NNP
The:DT   0.00 20.50 20.00 10.00 20.00 20.00 20.50
program:NN 20.00   9.07 15.00 20.00   5.83 14.98 10.50
financed:VBN 20.00 15.50 10.00 20.00 13.32   9.67 15.50
a:DT 10.00 20.50 20.00   8.73 20.00 20.00 20.50
grant:NN 20.00   9.60 15.00 20.00   6.89 14.87 10.50
the:DT   0.00 20.50 20.00 10.00 20.00 20.00 20.50
UN_Agency_for_International_Development:NNP 20.50   0.00 15.50 20.50   5.55 15.50 10.50
is:VBZ 20.00 15.50   0.00 20.00 15.00 10.00 15.50
aimed:VBN 20.00 15.50 10.00 20.00 13.37   8.74 15.50
at:IN 20.00 20.50 20.00 18.52 20.00 20.00 20.50
reducing:VBG 20.00 15.50 10.00 20.00 15.00   9.12 15.50
infant_mortality:NN 20.00 10.27 15.00 20.00   8.53 15.00 10.50
Madagascar:NNP 20.50 10.50 15.50 20.50 10.50 15.50   0.00
an:DT 10.00 20.50 20.00   0.00 20.00 20.00 20.50
island:NN 20.00   9.12 15.00 20.00   5.93 14.69 10.50
nation:NN 20.00   6.30 15.00 20.00   2.50 14.71 10.50
the:DT   0.00 20.50 20.00 10.00 20.00 20.00 20.50
Indian_Ocean:NNP 20.50 10.50 15.50 20.50 10.50 15.50 10.00
the:DT   0.00 20.50 20.00 10.00 20.00 20.00 20.50
eastern:JJ 20.00 12.50 12.00 20.00 11.43 11.71 12.50
coast:NN 20.00   9.66 15.00 20.00   7.00 13.05 10.50
Africa:NNP 20.50   9.69 15.50 20.50   7.59 15.50   9.07
NO_WORD   1.00 10.00   1.00   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.31 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.43 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Indian_Ocean" of "nation" dropped on aligned hyp word "organization"
 1.00  1.00 Hypernym.posWiden : widening in positive context: nation -> organization
-1.00  1.00 NullPunisher.other : based
-0.05  1.00 NullPunisher.aux : is
 1.00  1.00 Quant.contract : [an,an]
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "UN_Agency_for_International_Development" <-prep_from-- "grant" vs. hyp "UN_Agency_for_International_Development" <-nsubj-- "organization", which aligned to text "nation"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.7924
Threshold: -1.8794


Inference ID: 2004

Txt: Bosch was found innocent in Venezuela of the bombing charges for a third time Aug. 7.

Hyp: Bosch lives in Venezuela. (don't know)

Bosch
NNP
lives
VBZ
Venezuela
NNP
Bosch:NNP   0.50 15.50   9.91
was:VBD 15.50 10.00 15.50
found:VBN 15.50   7.21 15.50
innocent:JJ 12.50   6.54 12.50
Venezuela:NNP   9.91 15.50   0.00
the:DT 20.50 20.00 20.50
bombing:NN 10.38 11.95 10.29
charges:NNS   9.72 15.00   9.50
a:DT 20.50 20.00 20.50
third:JJ 12.50 12.00 12.50
time:NN   9.68 14.70   9.45
Aug.:NNP   9.87 15.50   9.67
7:CD 20.50 20.30 20.50
NO_WORD 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.04 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "charges" of "Venezuela" dropped on aligned hyp word "Venezuela"
-2.00  1.00 Location.mismatch : no clear info of matching: live(X, prep_in)
-1.00  1.00 NullPunisher.other : lives
-2.00  1.00 RootEntailment.unalignedRoot : "lives" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.0417
Threshold: -1.8794


Inference ID: 1918

Txt: The organizing committee said 65 countries have entered the Lillehammer Olympic Games, matching the number of nations at the 1992 Winter Games in Albertville, France.

Hyp: 65 countries take part in the Lillehammer Olympic Games. (yes)

65
CD
countries
NNS
take
VBP
part
NN
the
DT
Lillehammer
NNP
Olympic_Games
NNPS
The:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00
organizing:VBG 19.07 13.67   9.98 13.89 20.00 15.00 15.00
committee:NN 20.21   6.40 15.00   6.51 20.00 10.00   8.79
said:VBD 20.30 14.48   9.25 15.00 20.00 15.00 15.00
65:CD   0.00 20.43 18.89 20.50 20.50 20.50 20.50
countries:NNS 20.43   0.00 15.00   5.72 20.00 10.00   8.36
have:VBP 20.50 15.00   6.52 15.00 20.00 15.00 15.00
entered:VBN 20.50 14.53 10.00 15.00 20.00 15.00 15.00
the:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00
Lillehammer:NNP 20.50 10.00 15.00 10.00 20.00   0.00 10.00
Olympic_Games:NNPS 20.50   8.36 15.00   8.43 20.00 10.00   0.00
matching:VBG 19.31 15.00 10.00 15.00 20.00 15.00 15.00
the:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00
number:NN 18.43   6.69 15.00   5.83 20.00 10.00   8.94
nations:NNS 20.44   1.97 15.00   6.35 20.00 10.00   8.71
the:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00
1992:CD 10.40 20.50 20.50 19.78 20.50 20.50 20.50
Winter:NNPS 20.50   7.95 15.00   7.29 20.00 10.00   9.52
Games:NNPS 20.50   7.41 15.00   7.50 20.00 10.00   3.84
Albertville:NNP 20.50 10.50 15.50 10.50 20.50   7.83 10.50
France:NNP 20.50   3.79 15.50   8.11 20.50 10.50   9.84
NO_WORD 10.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.43 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : part
-1.00  1.00 NullPunisher.other : take
-2.00  1.00 RootEntailment.unalignedRoot : "take" not aligned to anything
Hand-tuned score (dot product of above): -1.5260
Threshold: -1.8794


Inference ID: 2075

Txt: Queen Victoria died in 1901, at Osborne House on the Isle of Wight.

Hyp: Queen Victoria was born in 1901 (don't know)

Queen_Victoria
NNP
was
VBD
born
VBN
1901
CD
Queen_Victoria:NNP   0.00 15.00 15.00 20.50
died:VBD 15.00 10.00   3.50 17.70
1901:CD 20.50 20.50 17.11   0.50
Osborne_House:NNP   7.58 15.50 10.50 20.50
the:DT 20.00 20.00 20.00 20.50
Isle_of_Wight:NNP   9.80 15.50 15.50 20.50
NO_WORD 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.97 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "born" aligned badly to "Osborne_House"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "1901" <-prep_in-- "died" vs. hyp "1901" <-prep_in-- "born", which aligned to text "Osborne_House" args have different parents, different relations: text "Queen_Victoria" <-nsubj-- "died" vs. hyp "Queen_Victoria" <-nsubjpass-- "born", which aligned to text "Osborne_House"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -6.0759
Threshold: -1.8794


Inference ID: 1658

Txt: DAYTON, Ohio. A cargo plane bound for Montreal with a small quantity of hazardous chemicals crashed and exploded shortly after takeoff.

Hyp: Dayton is located in Ohio. (yes)

Dayton
NNP
is
VBZ
located
VBN
Ohio
NNP
DAYTON_,_Ohio:NNP   0.00 15.50 15.50   5.00
A:DT 20.50 20.00 20.00 20.50
cargo:NN   9.68 15.00 12.37   9.11
plane:NN   9.68 15.00 13.67   9.11
bound:VBN 15.50 10.00   8.08 15.50
Montreal:NNP   5.81 15.50 15.50   6.70
a:DT 20.50 20.00 20.00 20.50
small:JJ 12.50 12.00   9.32 12.50
quantity:NN   8.44 15.00 13.69   7.51
hazardous:JJ 12.50 12.00 10.13 12.50
chemicals:NNS   9.06 15.00 13.95   8.29
crashed:VBD 15.50 10.00   7.54 15.50
exploded:VBD 15.50 10.00   7.61 15.50
shortly:RB 15.50 20.00 18.89 15.50
takeoff:NN 10.38 15.00 13.84 10.17
NO_WORD 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.46 Alignment.score
 1.00  0.81 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
 2.00  1.00 Location.match : "Ohio" matches "DAYTON_,_Ohio" (no subj unmatch)
-3.00  1.00 NullPunisher.entity : Dayton
-1.00  1.00 NullPunisher.other : located
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
 4.00  1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 1.0396
Threshold: -1.8794


Inference ID: 1600

Txt: Schroder Investment Management has indicated its intention to accept Revival's offer to buy retailer Marks & Spencer.

Hyp: Revival tries to take over Marks & Spencer Group. (yes)

Revival
NNP
tries
VBZ
to
TO
take
VB
Marks_&_Spencer_Group
NNP
Schroder_Investment_Management:NNP   7.63 15.50 20.50 15.50   8.19
has:VBZ 15.00 10.00 20.00 10.00 15.50
indicated:VBN 15.00 10.00 20.00   9.79 15.50
its:PRP$ 12.00 15.00 20.00 15.00 12.50
intention:NN   8.68 11.88 20.00 13.04   8.84
to:TO 20.00 20.00   0.00 20.00 20.50
accept:VB 15.00   7.92 20.00   7.00 15.50
Revival:NNP   0.50 15.50 20.50 15.50   9.05
offer:NN   8.65 14.29 20.00 14.58   8.81
to:TO 20.00 20.00   0.00 20.00 20.50
buy:VB 15.00   9.45 20.00   7.73 15.50
retailer:NN   9.09 15.00 20.00 14.58   4.91
Marks_&_Spencer:NNP   9.55 15.50 20.50 15.50   0.00
NO_WORD 10.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.81 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "retailer" of "Marks_&_Spencer" dropped on aligned hyp word "Marks_&_Spencer_Group"
-1.00  1.00 NullPunisher.other : take
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : tries
-2.00  1.00 RootEntailment.unalignedRoot : "tries" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.6080
Threshold: -1.8794


Inference ID: 1636

Txt: Sirhan Sirhan, a Jordanian immigrant, was convicted of murdering Kennedy and is serving a life term at Soledad Prison.

Hyp: Sirhan Sirhan killed Kennedy. (yes)

Sirhan_Sirhan
NNP
killed
VBD
Kennedy
NNP
Sirhan_Sirhan:NNP   0.00 15.50 10.50
a:DT 20.50 20.00 20.00
Jordanian:NNP 10.50 15.00 10.00
immigrant:NNP 10.50 13.13   8.56
was:VBD 15.50 10.00 15.00
convicted:VBN 15.50   4.33 15.00
of:IN 20.50 20.00 20.00
murdering:VBG 15.50   4.36 15.00
Kennedy:NNP 10.00 15.50   0.50
is:VBZ 15.50 10.00 15.00
serving:VBG 15.50   7.56 15.00
a:DT 20.50 20.00 20.00
life:NN 10.50 14.53   8.92
term:NN 10.50 15.00   9.17
Soledad_Prison:NNP 10.50 15.50 10.50
NO_WORD 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.10 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "immigrant" of "Sirhan_Sirhan" dropped on aligned hyp word "Sirhan_Sirhan"
 1.00  1.00 Hypernym.posWiden : widening in positive context: murder -> kill
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "killed" aligned badly to "murdering"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Sirhan_Sirhan" <-nsubjpass-- "convicted" vs. hyp "Sirhan_Sirhan" <-nsubj-- "killed", which aligned to text "murdering"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.6431
Threshold: -1.8794


Inference ID: 2001

Txt: Christoffer's family was identified only as an Air Force family living in Southern California.

Hyp: Air Force is an organisation based in Southern California. (don't know)

Air_Force
NNP
is
VBZ
an
DT
organization
NN
based
VBN
Southern_California
NNP
Christoffer:NNP 10.50 15.50 20.50 10.50 15.50 10.50
family:NN 10.50 15.00 20.00   2.81 15.00   8.11
was:VBD 15.50   0.00 20.00 15.00 10.00 15.50
identified:VBN 15.50 10.00 20.00 14.07   7.98 15.50
only:RB 15.50 20.00 20.00 15.00 20.00 15.50
an:DT 20.50 20.00   0.00 20.00 20.00 20.50
Air_Force:NNP   0.00 15.50 20.50 10.50 15.50 10.50
family:NN 10.50 15.00 20.00   2.81 15.00   8.11
living:NN 10.50 15.00 20.00   6.26 14.75   8.62
Southern_California:NNP 10.50 15.50 20.50   6.57 15.50   0.00
NO_WORD 10.00   1.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.04 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : based
-1.00  1.00 NullPunisher.other : organization
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "organization" not aligned to anything
Hand-tuned score (dot product of above): -2.5141
Threshold: -1.8794


Inference ID: 1910

Txt: Romano Prodi will meet the US President George Bush in his capacity as president of the European commission.

Hyp: George Bush is the president of the European Commission. (don't know)

George_Bush
NNP
is
VBZ
the
DT
president
NN
the
DT
European_Commission
NNP
Romano_Prodi:NNP 10.00 15.50 20.50 10.50 20.50 10.50
will:MD 20.50 20.00 10.00 20.00 10.00 20.50
meet:VB 15.50 10.00 20.00 14.71 20.00 15.50
the:DT 20.50 20.00   0.00 20.00   0.00 20.50
US:NNP   7.37 15.50 20.50   6.79 20.50   7.41
President:NNP   6.91 15.00 20.00   0.00 20.00   6.96
George_Bush:NNP   0.00 15.50 20.50   6.91 20.50   7.52
his:PRP$ 12.50 13.00 20.00 12.00 20.00 12.50
capacity:NN   9.05 15.00 20.00   8.18 20.00   9.08
president:NN   6.91 15.00 20.00   0.00 20.00   6.96
the:DT 20.50 20.00   0.00 20.00   0.00 20.50
European:JJ 12.50 12.00 20.00 12.00 20.00   7.50
commission:NN   8.15 15.00 20.00   7.16 20.00   5.50
NO_WORD 10.00   1.00   1.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.35 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "European" of "commission" dropped on aligned hyp word "European_Commission"
-1.00  1.00 Apposition.mismatch : no apposition in text between president and George_Bush
-0.10  1.00 NullPunisher.article : the
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "George_Bush" <-dobj-- "meet" vs. hyp "George_Bush" <-nsubj-- "president", which aligned to text "president"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.7381
Threshold: -1.8794


Inference ID: 1652

Txt: Rome is in Lazio province and Naples in Campania.

Hyp: Rome is located in Lazio province. (yes)

Rome
NNP
is
VBZ
located
VBN
Lazio
NNP
province
NN
Rome:NNP   0.00 15.50 15.50 10.00   6.39
is:VBZ 15.50   0.00 10.00 15.50 15.00
Lazio:NNP 10.00 15.50 15.50   0.00 10.50
province:NNP   6.39 15.00 12.90 10.50   0.00
Naples:NNP   6.64 15.00 15.00 10.50   6.28
Campania:NNP 10.00 15.50 15.50 10.00 10.50
NO_WORD 10.00   1.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.72 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-2.00  1.00 Location.mismatch : no clear info of matching: locate(X, prep_in)
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : located
-2.00  1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
Hand-tuned score (dot product of above): -2.3562
Threshold: -1.8794


Inference ID: 1933

Txt: Like the United States, U.N. officials are also dismayed that Aristide killed a conference called by Prime Minister Robert Malval in Port-au-Prince in hopes of bringing all the feuding parties together.

Hyp: U.N. officials take part in a conference called by Prime Minister Robert Malval. (don't know)

U.N.
NNP
officials
NNS
take_part
VBP
a
DT
conference
NN
called
VBN
Prime_Minister
NNP
Robert_Malval
NNP
the:DT 20.50 20.00 20.00 10.00 20.00 20.00 20.00 20.50
United_States:NNP   9.03   6.38 15.50 20.50   8.31 15.50   8.25   7.61
U.N.:NNP   0.00   9.00 15.50 20.50   8.55 15.50   9.93   9.64
officials:NNS   9.00   0.00 15.00 20.00   7.78 13.08   7.04   6.78
are:VBP 15.50 15.00 10.00 20.00 15.00 10.00 15.00 15.50
also:RB 15.50 15.00 20.00 20.00 15.00 20.00 15.00 15.50
dismayed:JJ 12.50   9.56 12.00 20.00   9.85   9.59 12.00 12.50
that:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.50
Aristide:NNP 10.50 10.50 15.50 20.50 10.50 15.50 10.50 10.00
killed:VBD 15.50 13.63 10.00 20.00 15.00   5.00 15.00 15.50
a:DT 20.50 20.00 20.00   0.00 20.00 20.00 20.00 20.50
conference:NN   8.55   7.78 15.00 20.00   0.00 13.34   9.01   9.10
called:VBD 15.50 13.08 10.00 20.00 13.34   0.00 15.00 15.50
Prime_Minister:NNP   9.93   7.04 15.00 20.00   9.01 15.00   0.00   8.53
Robert_Malval:NNP   9.64   6.78 15.50 20.50   9.10 15.50   8.53   0.00
Port-au-Prince:NNP 10.50 10.50 15.50 20.50 10.50 15.50 10.50 10.50
hopes:NNS   9.83   7.97 15.00 20.00   8.86 12.34   9.13   9.24
of:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.50
bringing:VBG 15.50 15.00 10.00 20.00 15.00   9.03 15.00 15.50
all:PDT 20.50 20.00 20.00 10.00 20.00 20.00 20.00 20.50
the:DT 20.50 20.00 20.00 10.00 20.00 20.00 20.00 20.50
feuding:NN 10.25   8.86 15.00 20.00   8.63 14.47   9.63   9.89
parties:NNS   7.96   7.23 15.00 20.00   7.04 15.00   8.66   8.68
together:RB 15.50 15.00 20.00 20.00 15.00 20.00 15.00 15.50
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.91 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.62 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Port-au-Prince" of "Robert_Malval" dropped on aligned hyp word "Robert_Malval"
-1.00  1.00 NullPunisher.other : take_part
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "take_part" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.3161
Threshold: -1.8794


Inference ID: 1619

Txt: Illinois born Charlton Heston was a 27-year-old actor from Broadway and television when he arrived in Hollywood for a five-picture contract with Hal Wallis.

Hyp: Charlton Heston was born in Illinois. (yes)

Charlton_Heston
NNP
was
VBD
born
VBN
Illinois
NNP
Illinois:NNP 10.50 15.00 15.00   0.50
born:NNP 10.50 15.00   0.00   9.89
Charlton_Heston:NNP   0.00 15.50 15.50 10.50
was:VBD 15.50   0.00 10.00 15.50
a:DT 20.50 20.00 20.00 20.50
27-year-old:JJ 12.50 12.00 10.79 12.50
actor:NN 10.50 15.00 12.33   8.38
Broadway:NNP 10.50 15.50 15.50   9.34
television:NN 10.50 15.00 15.00   8.84
when:WRB 20.50 20.00 20.00 20.50
he:PRP 12.50 15.00 15.00 12.50
arrived:VBD 15.50 10.00   5.54 15.50
Hollywood:NNP 10.50 15.50 15.50   9.23
a:DT 20.50 20.00 20.00 20.50
five-picture:JJ 12.50 12.00 11.99 12.50
contract:NN 10.50 15.00 14.91   8.90
Hal_Wallis:NNP 10.00 15.50 15.50 10.50
NO_WORD 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.54 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Charlton_Heston" <-nsubj-- "actor" vs. hyp "Charlton_Heston" <-nsubjpass-- "born", which aligned to text "born" args have different parents, different relations: text "Illinois" <-nsubj-- "actor" vs. hyp "Illinois" <-prep_in-- "born", which aligned to text "born"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -2.4121
Threshold: -1.8794


Inference ID: 1605

Txt: Deployment of workers to Iraq has been halted after the reported hostage-taking of a Filipino man by gunmen

Hyp: A Filipino man has been kidnapped by gunmen. (yes)

A
DT
Filipino
NNP
man
NN
has
VBZ
been
VBN
kidnapped
VBN
gunmen
NNS
Deployment:NNP 20.00   9.71   8.72 15.00 15.00 15.00   9.74
workers:NNS 20.00   6.87   3.79 15.00 15.00 13.87   6.97
Iraq:NNP 20.50   9.87   8.78 15.50 15.50 15.50   9.87
has:VBZ 20.00 15.00 15.00   0.00 10.00 10.00 15.00
been:VBN 20.00 15.00 15.00 10.00   0.00 10.00 15.00
halted:VBN 20.00 15.00 15.00 10.00 10.00   9.67 13.65
the:DT 10.00 20.00 20.00 20.00 20.00 20.00 20.00
reported:JJ 20.00 12.00 11.86 12.00 12.00 10.93   9.71
hostage-taking:NN 20.00   8.52   7.50 15.00 15.00 11.60   6.09
a:DT   0.00 20.00 20.00 20.00 20.00 20.00 20.00
Filipino:NNP 20.00   0.00   7.12 15.00 15.00 15.00   8.47
man:NN 20.00   7.12   0.00 15.00 15.00 10.14   7.22
gunmen:NNS 20.00   8.47   7.22 15.00 15.00   8.41   0.00
NO_WORD   1.00 10.00 10.00   1.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.68 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  0.57 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "hostage-taking" of "halted" dropped on aligned hyp word "kidnapped"
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "kidnapped" aligned badly to "halted"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "man" <-prep_of-- "hostage-taking" vs. hyp "man" <-nsubjpass-- "kidnapped", which aligned to text "halted"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.6249
Threshold: -1.8794


Inference ID: 1665

Txt: He graduated from high school from Benton, Tenn. and from Tennessee Tech. in Cookville, and holds a doctorate in physics from Virginia Tech.

Hyp: Tennessee Tech is an organisation based in Cookville. (yes)

Tennessee_Tech
NNP
is
VBZ
an
DT
organization
NN
based
VBN
Cookville
NNP
He:PRP 12.50 15.00 20.00 12.00 15.00 12.50
graduated:VBD 15.50 10.00 20.00 15.00   8.50 15.50
high_school:NN   9.74 15.00 20.00   4.73 15.00 10.50
Benton:NNP   9.85 15.50 20.50   8.44 15.50 10.00
Tenn.:NNP 10.50 15.50 20.50 10.50 15.50 10.00
Tennessee_Tech:NNP   0.50 15.50 20.50   7.90 15.50 10.00
Cookville:NNP 10.50 15.50 20.50 10.50 15.50   0.00
and:NNP 10.50 15.00 18.00 10.00 15.00 10.50
holds:VBZ 15.50 10.00 20.00 14.98   9.28 15.50
a:DT 20.50 20.00   8.73 20.00 20.00 20.50
doctorate:NN   9.89 15.00 20.00   7.29 14.36 10.50
physics:NN   9.65 15.00 20.00   6.73 14.41 10.50
Virginia_Tech:NNP   5.48 15.50 20.50   7.47 15.50 10.00
NO_WORD 10.00   1.00   1.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.98 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : based
-1.00  1.00 NullPunisher.other : organization
-0.10  1.00 NullPunisher.article : an
-2.00  1.00 RootEntailment.unalignedRoot : "organization" not aligned to anything
Hand-tuned score (dot product of above): -2.5610
Threshold: -1.8794


Inference ID: 1631

Txt: "Sculptures by Duane Hanson" is an exhibition of lifelike people sculptures created by this Minnesota born artist.

Hyp: Duane Hanson was born in Minnesota. (yes)

Duane_Hanson
NNP
was
VBD
born
VBN
Minnesota
NNP
Sculptures:NNP 10.50 15.00 15.00   9.35
Duane_Hanson:NNP   0.00 15.50 15.50 10.50
is:VBZ 15.50   0.00 10.00 15.50
an:DT 20.50 20.00 20.00 20.50
exhibition:NN 10.50 15.00 14.76   9.54
lifelike:JJ 12.50 12.00 11.56 12.50
people:NNS 10.50 15.00 14.49   8.60
sculptures:NNS 10.50 15.00 13.30   9.35
created:VBN 15.50 10.00   9.20 15.50
this:DT 20.50 20.00 20.00 20.50
Minnesota:NNP 10.50 15.50 15.50   0.00
born:NNP 10.50 15.00   0.00   9.92
artist:NN 10.50 15.00 11.82   8.63
NO_WORD 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.74 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Duane_Hanson" <-prep_by-- "Sculptures" vs. hyp "Duane_Hanson" <-nsubjpass-- "born", which aligned to text "born" args have different parents, different relations: text "Minnesota" <-prep_by-- "created" vs. hyp "Minnesota" <-prep_in-- "born", which aligned to text "born"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -2.2179
Threshold: -1.8794


Inference ID: 1946

Txt: Oil prices eased back from record high levels after Russia's justice ministry said embattled oil giant Yukos can continue oil production and sales.

Hyp: Oil prices rise. (don't know)

Oil
NNP
prices
NNS
rise
VBP
Oil:NNP   0.00   7.39 15.00
prices:NNS   7.39   0.00 10.32
eased:VBD 15.00 10.87   6.72
back:RB 15.00 13.63 19.81
record:JJ 12.00 10.74 10.10
high:JJ 12.00   9.12   9.31
levels:NNS   7.58   4.32   9.33
after:IN 20.00 18.48 20.00
Russia:NNP   9.04   9.01 15.50
justice:NN   7.84   7.29 15.00
ministry:NN   8.49   8.04 14.01
said:VBD 15.00 15.00   9.93
embattled:JJ 12.00 12.00 12.00
oil:NN   0.00   4.25 13.46
giant:NN   8.00   7.97 15.00
Yukos:NNP 10.50 10.50 15.50
can:MD 20.00 20.00 20.00
continue:VB 15.00 13.19   7.10
oil_production:NN   8.82   8.43 15.00
sales:NNS   7.62   4.82 11.62
NO_WORD 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.60 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : rise
-2.00  1.00 RootEntailment.unalignedRoot : "rise" not aligned to anything
Hand-tuned score (dot product of above): -0.3064
Threshold: -1.8794


Inference ID: 1626

Txt: As a real native Detroiter, I want to remind everyone that Madonna is from Bay City, Mich., a nice place in the thumb of the state's lower peninsula.

Hyp: Madonna was born in Bay City, Mich. (yes)

Madonna
NNP
was
VBD
born
VBN
Bay_City
NNP
Mich.
NNP
a:DT 20.50 20.00 20.00 20.50 20.50
real:JJ 12.50 12.00 10.94 12.50 12.50
native:JJ 12.50 12.00   5.50 12.50 12.50
Detroiter:NNP 10.00 15.50 15.50 10.50 10.50
Detroiter:NNP 10.00 15.50 15.50 10.50 10.50
want:VBP 15.50 10.00 10.00 15.50 15.50
to:TO 20.50 20.00 20.00 20.50 20.50
remind:VB 15.50 10.00   9.43 15.50 15.50
everyone:NN 10.50 15.00 15.00 10.50 10.50
that:IN 20.50 20.00 20.00 20.50 20.50
Madonna:NNP   0.00 15.50 15.50   9.31 10.50
is:VBZ 15.50   0.00 10.00 15.50 15.50
Bay_City:NNP   9.31 15.50 15.50   0.00 10.00
Mich.:NNP 10.50 15.50 15.50 10.00   0.00
a:DT 20.50 20.00 20.00 20.50 20.50
nice:JJ 12.50 12.00 11.99 12.50 12.50
place:NN   8.69 15.00 13.36   8.04 10.50
the:DT 20.50 20.00 20.00 20.50 20.50
thumb:NN   9.85 15.00 15.00   7.17 10.50
the:DT 20.50 20.00 20.00 20.50 20.50
state:NN   9.19 15.00 14.77   8.66 10.50
lower_peninsula:NN 10.50 15.00 15.00 10.50 10.50
NO_WORD 10.00   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.47 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : born
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 RootEntailment.unalignedRoot : "born" not aligned to anything
Hand-tuned score (dot product of above): -0.6188
Threshold: -1.8794


Inference ID: 1635

Txt: In 1865, John Wilkes Booth, the assassin of President Abraham Lincoln, was surrounded by federal troops near Bowling Green, Va., and killed.

Hyp: John Wilkes Booth killed President Abraham Lincoln. (yes)

John_Wilkes_Booth
NNP
killed
VBD
President_Abraham_Lincoln
NNP
1865:CD 20.50 20.50 20.50
John_Wilkes_Booth:NNP   0.00 15.50   8.95
the:DT 20.50 20.00 20.00
assassin:NN   0.72 11.18   8.41
President_Abraham_Lincoln:NNP   8.95 15.00   0.00
was:VBD 15.50 10.00 15.00
surrounded:VBN 15.50   5.41 15.00
federal:JJ 12.50 11.90 12.00
troops:NNS 10.01 12.12   9.11
Bowling_Green:NNP 10.50 15.50 10.50
Va.:NNP   9.75 15.50   9.25
killed:VBN 15.50   0.00 15.00
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.76 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "assassin" of "John_Wilkes_Booth" dropped on aligned hyp word "John_Wilkes_Booth"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "John_Wilkes_Booth" <-nsubjpass-- "surrounded" vs. hyp "John_Wilkes_Booth" <-nsubj-- "killed", which aligned to text "killed" args have different parents, different relations: text "President_Abraham_Lincoln" <-prep_of-- "assassin" vs. hyp "President_Abraham_Lincoln" <-dobj-- "killed", which aligned to text "killed"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.9682
Threshold: -1.8794


Inference ID: 1949

Txt: Oil prices fall back as Yukos oil threat lifted

Hyp: Oil prices rise. (don't know)

Oil
NNP
prices
NNS
rise
VBP
Oil:NNP   0.00   7.39 15.00
prices:NNS   7.39   0.00 10.32
fall_back:NN   9.06   8.72 15.00
as:IN 20.00 20.00 20.00
Yukos:NNP 10.50 10.50 15.50
oil:NN   0.00   4.25 13.46
threat:NN   8.12   8.09 13.58
lifted:VBD 15.00 11.37   6.29
NO_WORD 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.60 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : rise
-2.00  1.00 RootEntailment.unalignedRoot : "rise" not aligned to anything
Hand-tuned score (dot product of above): -0.3064
Threshold: -1.8794


Inference ID: 1917

Txt: There can be no doubt that the Administration already is weary of Aristide, a populist Roman Catholic priest who in December, 1990, won an overwhelming victory in Haiti's only democratic presidential election

Hyp: Haiti's only democratic presidential election took place in 1990. (yes)

Haiti
NNP
only
RB
democratic
JJ
presidential
JJ
election
NN
took_place
VBD
1990
CD
There:EX 20.50 20.00 20.00 20.00 20.00 20.00 20.50
can:MD 20.50 20.00 20.00 20.00 20.00 20.00 20.50
be:VB 15.50 20.00 12.00 12.00 15.00 10.00 20.50
no:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.50
doubt:NN 10.04 15.00 11.34 10.67   7.72 15.00 20.50
that:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.50
the:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.50
Administration:NNP   9.83 15.00 12.00 12.00   6.21 15.00 20.50
already:RB 15.50 10.00 12.00 12.00 15.00 20.00 20.50
is:VBZ 15.50 20.00 12.00 12.00 15.00 10.00 20.50
weary:JJ 12.50 12.00   9.76 10.00 12.00 12.00 20.50
Aristide:NNP 10.00 15.50 12.50 12.50 10.50 15.50 20.50
a:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.50
populist:JJ 12.50 12.00   4.54   5.26   6.78 12.00 19.00
Roman_Catholic:NNP 10.50 15.50 12.50 12.50 10.50 15.50 20.50
priest:NN   9.79 15.00 11.86 12.00   8.61 15.00 19.13
who:WP 12.50 20.00 15.00 15.00 12.00 15.00 20.50
December:NNP   9.99 15.50 12.50 12.50   9.09 15.50 20.00
1990:CD 20.50 20.50 19.53 19.51 19.19 20.50   0.00
won:VBD 15.50 20.00   9.22 10.01 12.41 10.00 17.14
an:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.50
overwhelming:JJ 12.50 12.00   5.88   6.90   8.66 12.00 19.65
victory:NN 10.03 15.00   8.81   8.36   6.73 15.00 18.99
Haiti:NNP   0.00 15.50 12.50 12.50 10.07 15.50 20.50
only:JJ 12.50   0.00 10.00 10.00 12.00 12.00 20.50
democratic:JJ 12.50 12.00   0.00   3.53   4.80 12.00 19.53
presidential:JJ 12.50 12.00   3.53   0.00   4.07 12.00 19.51
election:NN 10.07 15.00   4.80   4.07   0.00 15.00 19.19
NO_WORD 10.00   9.00   9.00   9.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.78 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.71 Alignment.txtSpan
 1.00  1.00 Date.matchDatesByGraph : hyp/txt matching, by graph: 1990 and children
-1.00  1.00 NullPunisher.other : took_place
-2.00  1.00 RootEntailment.unalignedRoot : "took_place" not aligned to anything
Hand-tuned score (dot product of above): 1.1875
Threshold: -1.8794


Inference ID: 1902

Txt: Sonia Gandhi can be defeated in the next elections in India by BJP.

Hyp: Sonia Gandhi is defeated by BJP. (don't know)

Sonia_Gandhi
NNP
is
VBZ
defeated
VBN
BJP
NNP
Sonia_Gandhi:NNP   0.00 15.50 15.50 10.50
can:MD 20.50 20.00 20.00 20.50
be:VB 15.50   0.00 10.00 15.50
defeated:VBN 15.50 10.00   0.00 15.50
the:DT 20.50 20.00 20.00 20.50
next:JJ 12.50 12.00 12.00 12.50
elections:NNS   9.61 15.00 10.55 10.50
India:NNP   9.59 15.50 15.50 10.50
BJP:NNP 10.50 15.50 15.50   0.00
NO_WORD 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.71 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "elections" of "defeated" dropped on aligned hyp word "defeated"
-2.00  1.00 Modal.dontKnow : possible -> actual
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.1198
Threshold: -1.8794


Inference ID: 1638

Txt: Yount, who was living in Coeur under the alias James Marvin Forsgren, was sentenced to life in prison for his conviction on first-degree murder and rape charges in the 1966 slaying of Pamela Sue Rimer, a student in his high school mathematics class in Luthersberg, Pa.

Hyp: James Marvin Forsgren killed Pamela Sue Rimer. (yes)

James_Marvin_Forsgren
NNP
killed
VBD
Pamela_Sue_Rimer
NNP
Yount:NNP 10.00 15.50 10.00
who:WP 12.50 15.00 12.50
was:VBD 15.50 10.00 15.50
living:VBG 15.50   7.31 15.50
Coeur:NNP 10.50 15.00 10.50
the:DT 20.50 20.00 20.50
alias:JJ 12.50   9.35 12.50
James_Marvin_Forsgren:NNP   0.00 15.50 10.00
was:VBD 15.50 10.00 15.50
sentenced:VBN 15.50   5.88 15.50
life:NN   8.91 14.53 10.50
prison:NN   8.61 11.09 10.50
his:PRP$ 12.50 15.00 12.50
conviction:NN   9.52 12.77 10.50
first-degree:JJ 12.50 12.00 12.50
murder:NN   9.03   9.95 10.50
rape:NN   8.77 10.27 10.50
charges:NNS   8.63 12.42 10.50
the:DT 20.50 20.00 20.50
1966:CD 20.50 19.59 20.50
slaying:NN   9.03   8.68 10.50
Pamela_Sue_Rimer:NNP 10.00 15.50   0.00
a:DT 20.50 20.00 20.50
student:NN   8.02 15.00 10.50
his:PRP$ 12.50 15.00 12.50
high_school:NN   9.29 15.00 10.50
mathematics:NN   9.23 15.00 10.50
class:NN   7.94 15.00 10.50
Luthersberg:NNP 10.50 15.50 10.50
Pa.:NNP   8.52 15.50 10.50
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.16 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "alias" of "James_Marvin_Forsgren" dropped on aligned hyp word "James_Marvin_Forsgren"
-1.00  1.00 NullPunisher.other : killed
-2.00  1.00 RootEntailment.unalignedRoot : "killed" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.0846
Threshold: -1.8794


Inference ID: 2015

Txt: BTA said those injured in the fire at Borovets, 22 miles southeast of Sofia, were from Britain, the Soviet Union, West Germany, the Netherlands, Greece and Denmark.

Hyp: Sofia is located in Britain. (don't know)

Sofia
NNP
is
VBZ
located
VBN
Britain
NNP
BTA:NNP 10.50 15.50 15.50 10.50
said:VBD 15.50 10.00   9.46 15.50
those:DT 20.50 20.00 20.00 20.50
injured:VBN 15.50 10.00   9.84 15.50
the:DT 20.50 20.00 20.00 20.50
fire:NN 10.24 15.00 11.90   9.29
Borovets:NNP 10.00 15.50 15.50 10.00
22:CD 20.50 20.50 20.50 20.50
miles:NNS 10.21 15.00   7.67   9.22
southeast:NN 10.38 15.00   8.87   9.67
Sofia:NNP   0.00 15.50 15.50   6.97
were:VBD 15.50   0.00 10.00 15.50
Britain:NNP   6.97 15.50 15.50   0.00
the:DT 20.50 20.00 20.00 20.50
Soviet_Union:NNP   7.30 15.50 15.50   5.42
West_Germany:NNP 10.00 15.50 15.50 10.00
the:DT 20.50 20.00 20.00 20.50
Netherlands:NNP   7.28 15.50 15.50   5.38
Greece:NNP   7.34 15.50 15.50   5.51
Denmark:NNP   7.48 15.50 15.50   5.83
NO_WORD 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.25 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "injured" of "southeast" dropped on aligned hyp word "located"
-2.00  1.00 Location.mismatch : no clear info of matching: locate(X, prep_in)
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "located" aligned badly to "southeast"
-1.00  1.00 Structure.relMismatch : text "Sofia" is prep_of of "southeast" while hyp "Sofia" is nsubjpass of "located" which aligned to text "southeast"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.7509
Threshold: -1.8794


Inference ID: 1914

Txt: Like the United States, U.N. officials are also dismayed that Aristide killed a conference called by Prime Minister Robert Malval in Port-au-Prince in hopes of bringing all the feuding parties together.

Hyp: Aristide kills Prime Minister Robert Malval (don't know)

Aristide
NNP
kills
NNP
Prime_Minister
NNP
Robert_Malval
NNP
the:DT 20.50 20.00 20.00 20.50
United_States:NNP 10.50   7.96   8.25   7.61
U.N.:NNP 10.00   9.54   9.93   9.64
officials:NNS 10.50   7.43   7.04   6.78
are:VBP 15.50 15.00 15.00 15.50
also:RB 15.50 15.00 15.00 15.50
dismayed:JJ 12.50 12.00 12.00 12.50
that:IN 20.50 20.00 20.00 20.50
Aristide:NNP   0.50 10.50 10.50 10.00
killed:VBD 15.50   6.00 15.00 15.50
a:DT 20.50 20.00 20.00 20.50
conference:NN 10.50   8.47   9.01   9.10
called:VBD 15.50 15.00 15.00 15.50
Prime_Minister:NNP 10.50   8.79   0.00   8.53
Robert_Malval:NNP 10.50   8.83   8.53   0.00
Port-au-Prince:NNP 10.50 10.50 10.50 10.50
hopes:NNS 10.50   8.62   9.13   9.24
of:IN 20.50 20.00 20.00 20.50
bringing:VBG 15.50 12.75 15.00 15.50
all:PDT 20.50 20.00 20.00 20.50
the:DT 20.50 20.00 20.00 20.50
feuding:NN 10.50   9.31   9.63   9.89
parties:NNS 10.50   8.02   8.66   8.68
together:RB 15.50 15.00 15.00 15.50
NO_WORD 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.71 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Port-au-Prince" of "Robert_Malval" dropped on aligned hyp word "Robert_Malval"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Aristide" <-nsubj-- "killed" vs. hyp "Aristide" <-nn-- "Robert_Malval", which aligned to text "Robert_Malval" args have different parents, different relations: text "killed" <-ccomp-- "dismayed" vs. hyp "kills" <-nn-- "Robert_Malval", which aligned to text "Robert_Malval" args have different parents, different relations: text "Prime_Minister" <-prep_by-- "called" vs. hyp "Prime_Minister" <-nn-- "Robert_Malval", which aligned to text "Robert_Malval"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.8986
Threshold: -1.8794


Inference ID: 1935

Txt: Evans Paul, the pro-Aristide mayor of Port-au-Prince, said he had been told that the money will go to American lobbyists, politicians and journalists to campaign against the return of Aristide, Haiti's first democratically elected president

Hyp: Aristide is the mayor of Port-au-Prince. (don't know)

Aristide
NNP
is
VBZ
the
DT
mayor
NN
Port-au-Prince
NNP
Evans_Paul:NNP 10.50 15.50 20.50   8.86 10.50
the:DT 20.00 20.00   0.00 20.00 20.50
pro-Aristide:JJ   7.00 12.00 20.00 12.00 12.50
mayor:NN 10.00 15.00 20.00   0.00 10.50
Port-au-Prince:NNP 10.50 15.50 20.50 10.50   0.00
said:VBD 15.00 10.00 20.00 14.98 15.50
he:PRP 12.00 15.00 18.00 12.00 12.50
had:VBD 15.00 10.00 20.00 15.00 15.50
been:VBN 15.00   0.00 20.00 15.00 15.50
told:VBN 15.00 10.00 20.00 14.71 15.50
that:IN 20.00 20.00 20.00 20.00 20.50
the:DT 20.00 20.00   0.00 20.00 20.50
money:NN 10.00 15.00 20.00   8.30 10.50
will:MD 20.00 20.00 10.00 20.00 20.50
go:VB 15.00 10.00 20.00 15.00 15.50
American:NNP 10.50 15.50 20.50   7.93 10.00
lobbyists:NNS 10.00 15.00 20.00   8.36 10.50
politicians:NNS 10.00 15.00 20.00   1.60 10.50
journalists:NNS 10.00 15.00 20.00   7.68 10.50
campaign:NN 10.00 15.00 20.00   8.39 10.50
the:DT 20.00 20.00   0.00 20.00 20.50
return:NN 10.00 15.00 20.00   8.82 10.50
Aristide:NNP   0.50 15.50 20.50 10.50 10.00
Haiti:NNP 10.50 15.50 20.50   9.86 10.00
first:JJ 12.00 12.00 20.00 12.00 12.50
democratically:RB 15.00 20.00 20.00 15.00 15.50
elected:VBN 15.00 10.00 20.00 12.72 15.50
president:NN 10.00 15.00 20.00   5.16 10.50
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.73 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "pro-Aristide" of "mayor" dropped on aligned hyp word "mayor"
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Aristide" <-prep_of-- "return" vs. hyp "Aristide" <-nsubj-- "mayor", which aligned to text "mayor"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.1220
Threshold: -1.8794


Inference ID: 1612

Txt: With South Carolina being Jesse Jackson's home state, there was a very strong incentive in the black community.

Hyp: Jesse Jackson was born in South Carolina. (yes)

Jesse_Jackson
NNP
was
VBD
born
VBN
South_Carolina
NNP
With:IN 20.50 20.00 20.00 20.50
South_Carolina:NNP 10.50 15.50 15.50   0.00
being:VBG 15.50   0.00 10.00 15.50
Jesse_Jackson:NNP   0.00 15.50 15.50 10.50
home:NN   8.99 15.00 11.59 10.50
state:NN   9.23 15.00 14.77 10.50
there:EX 20.50 20.00 20.00 20.50
was:VBD 15.50   0.00 10.00 15.50
a:DT 20.50 20.00 20.00 20.50
very:RB 15.50 20.00 20.00 15.50
strong:JJ 12.50 12.00 12.00 12.50
incentive:NN   9.83 15.00 14.95 10.50
the:DT 20.50 20.00 20.00 20.50
black:JJ 12.50 12.00 12.00 12.50
community:NN   9.29 15.00 12.45 10.50
NO_WORD 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.29 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "state" of "home" dropped on aligned hyp word "born"
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "born" aligned badly to "home"
-1.00  1.00 Structure.relMismatch : text "Jesse_Jackson" is poss of "home" while hyp "Jesse_Jackson" is nsubjpass of "born" which aligned to text "home"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.3215
Threshold: -1.8794


Inference ID: 1934

Txt: Evans Paul, the pro-Aristide mayor of Port-au-Prince, said he had been told that the money will go to American lobbyists, politicians and journalists to campaign against the return of Aristide, Haiti's first democratically elected president

Hyp: Evans Paul is the mayor of Port-au-Prince. (yes)

Evans_Paul
NNP
is
VBZ
the
DT
mayor
NN
Port-au-Prince
NNP
Evans_Paul:NNP   0.00 15.50 20.50   8.86 10.50
the:DT 20.50 20.00   0.00 20.00 20.50
pro-Aristide:JJ 12.50 12.00 20.00 12.00 12.50
mayor:NN   8.86 15.00 20.00   0.00 10.50
Port-au-Prince:NNP 10.50 15.50 20.50 10.50   0.50
said:VBD 15.50 10.00 20.00 14.98 15.50
he:PRP 12.50 15.00 18.00 12.00 12.50
had:VBD 15.50 10.00 20.00 15.00 15.50
been:VBN 15.50   0.00 20.00 15.00 15.50
told:VBN 15.50 10.00 20.00 14.71 15.50
that:IN 20.50 20.00 20.00 20.00 20.50
the:DT 20.50 20.00   0.00 20.00 20.50
money:NN   9.06 15.00 20.00   8.30 10.50
will:MD 20.50 20.00 10.00 20.00 20.50
go:VB 15.50 10.00 20.00 15.00 15.50
American:NNP   8.26 15.50 20.50   7.93 10.50
lobbyists:NNS   8.94 15.00 20.00   8.36 10.50
politicians:NNS   8.42 15.00 20.00   1.60 10.50
journalists:NNS   8.49 15.00 20.00   7.68 10.50
campaign:NN   9.58 15.00 20.00   8.39 10.50
the:DT 20.50 20.00   0.00 20.00 20.50
return:NN   9.53 15.00 20.00   8.82 10.50
Aristide:NNP 10.50 15.50 20.50 10.50 10.50
Haiti:NNP   9.89 15.50 20.50   9.86 10.50
first:JJ 12.50 12.00 20.00 12.00 12.50
democratically:RB 15.50 20.00 20.00 15.00 15.50
elected:VBN 15.50 10.00 20.00 12.72 15.50
president:NN   8.14 15.00 20.00   5.16 10.50
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.89 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "pro-Aristide" of "mayor" dropped on aligned hyp word "mayor"
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Evans_Paul" <-nsubj-- "said" vs. hyp "Evans_Paul" <-nsubj-- "mayor", which aligned to text "mayor"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.9326
Threshold: -1.8794


Inference ID: 1637

Txt: Twenty years ago, on June 6, 1968, Sen. Robert F. Kennedy died at Good Samaritan Hospital in Los Angeles, 25 -LCB- hours after he was shot at the Ambassador Hotel by Sirhan Bishara Sirhan.

Hyp: Sirhan Bishara Sirhan killed Sen. Robert F. Kennedy (yes)

Sirhan_Bishara_Sirhan
NNP
killed
NNP
Sen.
NNP
Robert_F._Kennedy
NNP
Twenty:CD 20.50 20.50 20.50 20.50
years:NNS 10.50   7.01   6.37   8.37
ago:RB 15.50 13.97 15.00 15.50
June:NNP 10.50   8.32   7.78   9.02
6:CD 20.50 20.50 20.50 20.50
1968:CD 20.50 19.25 20.50 20.50
Sen.:NNP 10.50   7.95   0.00   9.12
Robert_F._Kennedy:NNP 10.00   8.25   9.12   0.00
died:VBD 15.50   8.80 15.00 15.50
Good_Samaritan_Hospital:NNP 10.50   9.75 10.17   9.01
Los_Angeles:NNP 10.50 10.50 10.50 10.50
25:CD 20.50 20.50 20.50 20.50
hours:NNS 10.50   7.69   7.14   8.92
he:PRP 12.50 12.00 12.00 12.50
was:VBD 15.50 15.00 15.00 15.50
shot:VBN 15.50 11.42 15.00 15.50
the:DT 20.50 20.00 20.00 20.50
Ambassador:NNP 10.50   8.61   9.24   8.77
Hotel:NNP 10.50   7.32   8.30   8.01
Sirhan_Bishara_Sirhan:NNP   0.00 10.50 10.50 10.00
NO_WORD 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.70 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added killed[killed-NNP]
-1.00  1.00 NullPunisher.other : killed
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Sirhan_Bishara_Sirhan" <-agent-- "shot" vs. hyp "Sirhan_Bishara_Sirhan" <-nn-- "Robert_F._Kennedy", which aligned to text "Robert_F._Kennedy" args have different parents, different relations: text "Sen." <-nsubj-- "died" vs. hyp "Sen." <-nn-- "Robert_F._Kennedy", which aligned to text "Robert_F._Kennedy"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.2098
Threshold: -1.8794


Inference ID: 1951

Txt: Crude oil prices shot to new highs yesterday as markets reacted to a threat by Russian authorities to shut down most of the production from that country's largest oil company

Hyp: Crude oil prices rise. (yes)

Crude_oil
NNS
prices
NNS
rise
VBP
Crude_oil:NNS   0.00   4.25 13.46
prices:NNS   4.25   0.00 10.32
shot:VBD 15.00 15.00   9.55
new:JJ 11.44 11.52 11.98
highs:NNS   7.27   4.13 11.73
yesterday:NN   8.31   7.23 14.32
markets:NNS   7.87   6.65 14.75
reacted:VBD 15.00 13.40   9.10
a:DT 20.00 20.00 20.00
threat:NN   8.60   8.09 13.58
Russian:NNP   9.46   9.04 15.50
authorities:NNS   7.70   6.42 14.73
to:TO 20.00 20.00 20.00
shut_down:VB 12.79 15.00 10.00
most:JJS 12.00 12.00 12.00
the:DT 20.00 20.00 20.00
production:NN   8.19   6.86 12.83
that:DT 20.00 20.00 20.00
country:NN   7.00   6.22 14.70
largest:JJS 10.26 12.00 12.00
oil_company:NN 10.00 10.00 15.00
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.60 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : rise
-2.00  1.00 RootEntailment.unalignedRoot : "rise" not aligned to anything
Hand-tuned score (dot product of above): -0.3064
Threshold: -1.8794


Inference ID: 1929

Txt: Teenage sensation Wayne Rooney powered England into the quarter-finals of Euro 2004 with two goals in Monday's 4-2 defeat of Croatia and they were joined in the last eight by champions France who beat Switzerland 3-1.

Hyp: France participates in Euro 2004. (yes)

France
NNP
participates
VBZ
Euro
NNP
2004
CD
Teenage:NNP   9.94 15.00 10.31 20.50
sensation:NNP   9.15 15.00   9.84 20.15
Wayne_Rooney:NNP   9.57 15.50 10.25 20.50
powered:VBN 15.50 10.00 15.50 20.50
England:NNP   2.67 15.50 10.04 20.50
the:DT 20.50 20.00 20.50 20.50
quarter-finals:NNS 10.50 14.33 10.50 19.93
Euro:NNP   9.95 15.50   0.00 20.00
2004:CD 20.50 19.59 20.00   0.00
two:CD 20.50 20.50 20.50 10.50
goals:NNS   8.30 11.56   9.26 20.50
Monday:NNP   9.07 15.50   7.73 20.00
4-2:JJ 12.50 11.73 12.50 19.11
defeat:NN   9.54 15.00 10.09 20.25
Croatia:NNP   7.83 15.50 10.50 20.50
they:PRP 12.50 15.00 12.50 20.50
were:VBD 15.50 10.00 15.50 20.50
joined:VBN 15.50   8.42 15.50 20.50
the:DT 20.50 20.00 20.50 20.50
last:JJ 12.50 12.00 12.50 20.50
eight:CD 20.50 20.50 20.50 10.50
champions:NNP   9.39 14.17 10.17 20.34
France:NNP   0.00 15.50   9.95 20.50
who:WP 12.50 15.00 12.50 20.50
beat:VBD 15.50   9.98 15.50 20.50
Switzerland:NNP   2.67 15.50 10.04 20.50
3-1:CD 20.50 20.24 20.50   9.28
NO_WORD 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.37 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "beat" of "France" dropped on aligned hyp word "France"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/2004
-1.00  1.00 NullPunisher.other : participates
-2.00  1.00 RootEntailment.unalignedRoot : "participates" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.9369
Threshold: -1.8794


Inference ID: 2011

Txt: Los Angeles Mayor James Hahn, along with top members of the Automobile Club of Southern California, along with will announce the kickoff of a $9 billion plan to fund Los Angeles County's Top Ten Traffic Busters.

Hyp: James Hahn is a member of the Automobile Club of Southern California. (don't know)

James_Hahn
NNP
is
VBZ
a
DT
member
NN
the
DT
Automobile_Club_of_Southern_California
NNP
Los_Angeles:NNP 10.50 15.50 20.50 10.50 20.50 10.50
Mayor:NNP   8.51 15.00 20.00   7.35 20.00   8.35
James_Hahn:NNP   0.00 15.50 20.50   7.30 20.50   7.86
top:JJ 12.50 12.00 20.00 10.01 20.00 12.50
members:NNS   7.30 15.00 18.71   0.00 18.72   7.10
the:DT 20.50 20.00 10.00 20.00   0.00 20.50
Automobile_Club_of_Southern_California:NNP   7.86 15.50 20.50   7.10 20.50   0.00
along:IN 20.50 20.00 20.00 20.00 20.00 20.50
with:IN 20.50 20.00 18.45 20.00 20.00 20.50
will:MD 20.50 20.00 10.00 20.00 10.00 20.50
announce:VB 15.50 10.00 20.00 15.00 20.00 15.50
the:DT 20.50 20.00 10.00 20.00   0.00 20.50
kickoff:NN 10.50 15.00 20.00 10.00 20.00 10.50
a:DT 20.50 20.00   0.00 20.00 10.00 20.50
$:$ 20.50 20.50 10.50 20.50 10.50 20.50
9:CD 20.50 20.50 20.50 20.50 20.50 20.50
billion:CD 20.50 20.50 20.50 20.50 20.50 20.50
plan:NN   7.96 15.00 20.00   6.72 20.00   7.22
to:TO 20.50 20.00 10.00 20.00 10.00 20.50
fund:VB 15.50 10.00 20.00 14.96 20.00 15.50
Los_Angeles_County:NNP 10.50 15.50 20.50 10.50 20.50 10.00
Top_Ten_Traffic_Busters:NNP   8.14 15.50 20.50   7.42 20.50   6.92
NO_WORD 10.00   1.00   1.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.59 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "top" of "members" dropped on aligned hyp word "member"
-1.00  1.00 Apposition.mismatch : no apposition in text between member and James_Hahn
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "James_Hahn" <-nsubj-- "announce" vs. hyp "James_Hahn" <-nsubj-- "member", which aligned to text "members"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.4779
Threshold: -1.8794


Inference ID: 1639

Txt: Lennon was murdered by Mark David Chapman outside the Dakota on Dec. 8, 1980.

Hyp: Mark David Chapman killed Lennon. (yes)

Mark_David_Chapman
NNP
killed
VBD
Lennon
NNP
Lennon:NNP 10.50 15.00   0.50
was:VBD 15.50 10.00 15.50
murdered:VBN 15.50   2.14 15.50
Mark_David_Chapman:NNP   0.00 15.50 10.50
outside:IN 20.50 18.83 20.50
the:DT 20.50 20.00 20.50
Dakota:NNP   9.94 15.50 10.00
Dec.:NNP   9.13 15.50 10.50
8:CD 20.50 20.50 20.50
1980:CD 20.50 19.66 20.50
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.02 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : killed
-2.00  1.00 RootEntailment.unalignedRoot : "killed" not aligned to anything
Hand-tuned score (dot product of above): -1.0656
Threshold: -1.8794


Inference ID: 1939

Txt: The plan was released by Mr Dean on behalf of the Secretary of Health and Human Services, Tommy Thompson, still recovering from a recent accident, at a Secretarial Summit on Health Information Technology that was attended by many of the nation's leaders in electronic health records.

Hyp: Mr Dean is the Secretary of Health and Human Services. (don't know)

Mr_Dean
NNP
is
VBZ
the
DT
Secretary
NNP
Health_and_Human_Services
NNP
The:DT 20.50 20.00   0.00 20.00 20.50
plan:NN   6.52 15.00 20.00   7.51   9.18
was:VBD 15.50   0.00 20.00 15.00 15.50
released:VBN 15.50 10.00 20.00 15.00 15.50
Mr_Dean:NNP   0.00 15.50 20.50   6.68   9.19
behalf:NN   8.62 15.00 20.00   9.03 10.14
the:DT 20.50 20.00   0.00 20.00 20.50
Secretary:NNP   6.68 15.00 20.00   0.00   9.90
Health_and_Human_Services:NNP   9.19 15.50 20.50   9.90   0.00
Tommy_Thompson:NNP   6.91 15.50 20.50   8.68 10.12
still:RB 15.50 20.00 20.00 15.00 15.50
recovering:VBG 15.50 10.00 20.00 15.00 15.50
a:DT 20.50 20.00 10.00 20.00 20.50
recent:JJ 12.50 12.00 20.00 12.00 12.50
accident:NN   7.91 15.00 20.00   8.53   9.85
a:DT 20.50 20.00 10.00 20.00 20.50
Secretarial_Summit_on_Health_Information_Technology:NNP   8.14 15.50 20.50   9.19   9.44
that:WDT 20.50 20.00 10.00 20.00 20.50
was:VBD 15.50   0.00 20.00 15.00 15.50
attended:VBN 15.50 10.00 20.00 15.00 15.50
many:JJ 12.50 12.00 20.00 12.00 12.50
the:DT 20.50 20.00   0.00 20.00 20.50
nation:NN   6.94 15.00 20.00   7.83   6.90
leaders:NNS   7.92 15.00 20.00   8.54   8.62
electronic:JJ 12.50 12.00 20.00 12.00 12.50
health:NN   7.92 15.00 20.00   8.54   9.85
records:NNS   6.96 15.00 20.00   7.84   9.40
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.62 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Tommy_Thompson" of "Secretary" dropped on aligned hyp word "Secretary"
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Mr_Dean" <-agent-- "released" vs. hyp "Mr_Dean" <-nsubj-- "Secretary", which aligned to text "Secretary" mismatched args with same relation prep_of args have different parents, different relations: text "Health_and_Human_Services" <-dep-- "Tommy_Thompson" vs. hyp "Health_and_Human_Services" <-prep_of-- "Secretary", which aligned to text "Secretary"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.2388
Threshold: -1.8794


Inference ID: 2002

Txt: In 1973, the United States and Communist China agreed to establish liaison offices in Beijing and Washington.

Hyp: Communist China is an organisation based in Beijing and Washington. (don't know)

Communist_China
NNP
is
VBZ
an
DT
organization
NN
based
VBN
Beijing
NNP
Washington
NNP
1973:CD 20.50 20.50 20.50 19.59 20.25 20.50 20.50
the:DT 20.00 20.00 10.00 20.00 20.00 20.50 20.50
United_States:NNP   4.07 15.50 20.50   5.63 15.50   5.56   4.64
Communist_China:NNP   0.50 15.50 20.50   6.99 15.50   6.78   6.38
agreed:VBD 15.00 10.00 20.00 15.00   8.21 15.50 15.50
to:TO 20.00 20.00   8.20 20.00 20.00 20.50 20.50
establish:VB 15.00 10.00 20.00 12.72   9.22 15.50 15.50
liaison:NN   8.89 15.00 20.00   7.13 15.00   9.72   9.37
offices:NNS   7.75 15.00 20.00   5.97 12.89   8.76   8.22
Beijing:NNP   6.78 15.50 20.50   7.67 15.50   0.00   3.13
Washington:NNP   6.38 15.50 20.50   6.96 15.50   3.13   0.00
NO_WORD 10.00   1.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.14 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : organization
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : based
-2.00  1.00 RootEntailment.unalignedRoot : "organization" not aligned to anything
Hand-tuned score (dot product of above): -2.2898
Threshold: -1.8794


Inference ID: 2083

Txt: The Irish Sales, Services and Marketing Operation of Microsoft was established in 1991.

Hyp: Microsoft was established in 1991. (don't know)

Microsoft
NNP
was
VBD
established
VBN
1991
CD
The:DT 20.50 20.00 20.00 20.50
Irish_Sales:NNP 10.00 15.50 15.50 20.50
Services_and_Marketing_Operation_of_Microsoft:NNP 10.00 15.50 15.50 20.50
was:VBD 15.50   0.00 10.00 20.50
established:VBN 15.50 10.00   0.00 16.71
1991:CD 20.50 20.50 16.71   0.00
NO_WORD 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.07 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1991
-3.00  1.00 NullPunisher.entity : Microsoft
Hand-tuned score (dot product of above): -0.2644
Threshold: -1.8794


Inference ID: 2013

Txt: P. Prayong, who like Kevala belongs to the Theravada sect of Buddhism, chose India over other Buddhist majority nations as it is the birthplace of Gautama Buddha.

Hyp: P. Prayong is a member of Theravada. (yes)

P._Prayong
NNP
is
VBZ
a
DT
member
NN
Theravada
NNP
P._Prayong:NNP   0.00 15.50 20.50   8.31 10.50
who:WP 12.50 15.00 20.00 12.00 12.50
Kevala:NNP 10.00 15.50 20.50 10.50 10.50
belongs:VBZ 15.50 10.00 20.00 12.26 15.50
the:DT 20.50 20.00 10.00 20.00 20.50
Theravada:NNP 10.50 15.50 20.50 10.50   0.00
sect:NN   9.03 15.00 20.00   7.69 10.50
Buddhism:NNP 10.50 15.50 20.50 10.50 10.00
chose:VBD 15.50 10.00 20.00 13.74 15.50
India:NNP   9.32 15.50 20.50   8.59 10.00
other:JJ 12.50 12.00 20.00 12.00 12.50
Buddhist:NNP 10.50 15.00 20.00 10.00 10.50
majority:NN   9.32 15.00 20.00   6.32 10.50
nations:NNS   8.56 15.00 20.00   7.09 10.50
it:PRP 12.50 15.00 20.00 12.00 12.50
is:VBZ 15.50   0.00 20.00 15.00 15.50
the:DT 20.50 20.00 10.00 20.00 20.50
birthplace:NN   9.85 15.00 20.00   8.94 10.50
Gautama_Buddha:NNP   9.90 15.50 20.50   8.97 10.00
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.20 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Buddhism" of "sect" dropped on aligned hyp word "member"
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : is
 1.00  1.00 Quant.contract : [the,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "member" aligned badly to "sect"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "P._Prayong" <-nsubj-- "belongs" vs. hyp "P._Prayong" <-nsubj-- "member", which aligned to text "sect" args have different parents but same relations: text "P._Prayong" <-nsubj-- "chose" vs. hyp "P._Prayong" <-nsubj-- "member", which aligned to text "sect" args have different parents, different relations: text "Theravada" <-prep_to-- "belongs" vs. hyp "Theravada" <-prep_of-- "member", which aligned to text "sect"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.0124
Threshold: -1.8794


Inference ID: 1927

Txt: The organizers of the 15th International AIDS Conference, scheduled for next month in Bangkok, Thailand, on Saturday responded to press reports that a prominent hotel in Bangkok discriminated against HIV-positive visitors attending a different conference this month.

Hyp: HIV-positive visitors take part in the 15th International AIDS Conference. (don't know)

HIV-positive
JJ
visitors
NNS
take
VBP
part
NN
the
DT
15th
JJ
International
NNP
AIDS
NNP
Conference
NNP
The:DT 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
organizers:NNS 12.00   5.50 13.84   8.46 20.00 11.07   9.17   9.52   9.32
the:DT 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
15th:JJ 10.00 10.33 11.38 12.00 20.00   0.00 12.00 12.00 12.00
International:NNP 12.00   7.37 15.00   7.21 20.00 12.00   0.00   8.84   7.49
AIDS:NNP 12.00   8.11 15.00   7.98 20.00 12.00   8.84   0.00   9.02
Conference:NNP 12.00   7.67 15.00   7.53 20.00 12.00   7.49   9.02   0.00
scheduled:VBN 10.67 12.53   6.49 14.09 20.00   9.44 15.00 15.00 15.00
next:JJ 10.00 12.00 12.00 12.00 20.00 10.00 12.00 12.00 12.00
month:NN 12.00   6.13 14.95   4.84 20.00 11.04   7.29   8.05   7.60
Bangkok:NNP 12.50   9.02 15.50   9.27 20.50 12.50   9.88 10.17 10.01
Thailand:NNP 12.50   8.96 15.50   9.22 20.50 12.50   9.85 10.15   9.98
Saturday:NNP 12.50   8.53 15.50   7.65 20.50 12.50   9.28   9.73   9.47
responded:VBD 12.00 15.00 10.00 14.70 20.00 12.00 15.00 15.00 15.00
to:TO 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
press:VB 10.58 12.98   8.60 15.00 20.00 11.65 15.00 15.00 15.00
reports:NNS   9.14   7.09 15.00   5.45 20.00 12.00   8.05   8.67   8.31
that:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
a:DT 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
prominent:JJ   9.82 12.00 12.00 11.50 20.00   9.22 12.00 12.00 12.00
hotel:NN 12.00   4.91 15.00   6.87 20.00 11.63   8.01   8.63   8.27
Bangkok:NNP 12.50   9.02 15.50   9.27 20.50 12.50   9.88 10.17 10.01
discriminated:VBD 12.00 15.00   8.05 13.86 20.00 10.83 15.00 15.00 15.00
HIV-positive:JJ   0.00 12.00 10.91 10.93 20.00 10.00 12.00 12.00 12.00
visitors:NNS 12.00   0.00 14.98   6.04 20.00 10.33   7.37   8.11   7.67
attending:VBG 11.12 13.57   9.15 15.00 20.00   9.60 15.00 15.00 15.00
a:DT 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
different:JJ   7.18 12.00 11.33 11.52 20.00 10.00 12.00 12.00 12.00
conference:NN   8.25   7.67 15.00   7.53 20.00   9.68   7.49   9.02   0.00
this:DT 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
month:NN 12.00   6.13 14.95   4.84 20.00 11.04   7.29   8.05   7.60
NO_WORD   9.00 10.00 10.00 10.00   1.00   9.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.74 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.56 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : part
-1.00  1.00 NullPunisher.other : take
-2.00  1.00 RootEntailment.unalignedRoot : "take" not aligned to anything
Hand-tuned score (dot product of above): -0.9679
Threshold: -1.8794


Inference ID: 1930

Txt: The G8 summit, held June 8-10, brought together leaders of the world's major industrial democracies, including Canada, France, Germany, Italy, Japan, Russia, United Kingdom, European Union and United States.

Hyp: Canada participates in the G8 summit. (yes)

Canada
NNP
participates
VBZ
the
DT
G8
NNP
summit
NN
The:DT 20.50 20.00   0.00 20.00 20.00
G8:NNP   9.10 15.00 20.00   0.00   8.66
summit:NN   9.21 14.53 20.00   8.66   0.00
held:VBN 15.50   8.94 20.00 15.00 13.26
June:NNP   9.11 15.50 20.50   7.61   9.17
8-10:CD 20.50 20.50 20.50 20.50 20.50
brought_together:VBD 15.50 10.00 20.00 15.00 15.00
leaders:NNS   9.06 15.00 20.00   8.51   6.28
the:DT 20.50 20.00   0.00 20.00 20.00
world:NN   8.76 13.30 20.00   8.20   8.34
major:JJ 12.50 11.07 20.00 12.00 11.58
industrial:JJ 12.50 12.00 20.00 12.00 12.00
democracies:NNS   9.49 15.00 20.00   8.94   5.74
including:VBG 15.50   8.63 20.00 15.00 15.00
Canada:NNP   0.00 15.50 20.50   9.10   9.21
France:NNP   5.02 15.50 20.50   9.19   9.30
Germany:NNP   4.91 15.50 20.50   9.09   9.21
Italy:NNP   5.25 15.50 20.50   9.38   9.48
Japan:NNP   7.35 15.50 20.50   8.80   8.93
Russia:NNP   5.52 15.50 20.50   9.61   9.69
United_Kingdom:NNP   4.78 15.50 20.50   8.99   9.11
European_Union:NNP   8.51 15.50 20.50   8.96   9.08
United_States:NNP   2.43 15.50 20.50   8.19   8.35
NO_WORD 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.65 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "held" of "summit" dropped on aligned hyp word "summit"
-1.00  1.00 NullPunisher.other : participates
-2.00  1.00 RootEntailment.unalignedRoot : "participates" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.8402
Threshold: -1.8794


Inference ID: 1931

Txt: Three of its five permanent members, France, Russia and China, are pushing the U.N. Security Council to lift the embargo on Iraq.

Hyp: China is a member of the U.N. Security Council. (yes)

China
NNP
is
VBZ
a
DT
member
NN
the
DT
U.N._Security_Council
NNP
Three:CD 20.50 20.50 20.50 20.50 20.50 20.50
its:PRP$ 12.50 13.00 20.00 12.00 20.00 12.50
five:CD 20.50 20.50 20.50 19.83 20.50 20.50
permanent:JJ 12.50 12.00 20.00 10.22 20.00 12.50
members:NNS   7.99 15.00 18.71   0.00 18.72   9.26
France:NNP   4.95 15.50 20.50   8.21 20.50   9.77
Russia:NNP   5.46 15.50 20.50   8.81 20.50 10.05
China:NNP   0.00 15.50 20.50   7.99 20.50   9.65
are:VBP 15.50   0.00 20.00 15.00 20.00 15.50
pushing:VBG 15.50 10.00 20.00 15.00 20.00 15.50
the:DT 20.50 20.00 10.00 20.00   0.00 20.50
U.N._Security_Council:NNP   9.65 15.50 20.50   9.26 20.50   0.00
to:TO 20.50 20.00 10.00 20.00 10.00 20.50
lift:VB 15.50 10.00 20.00 15.00 20.00 15.50
the:DT 20.50 20.00 10.00 20.00   0.00 20.50
embargo:NN   9.80 15.00 20.00   8.95 20.00 10.18
Iraq:NNP   3.76 15.50 20.50   9.21 20.50 10.22
NO_WORD 10.00   1.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "permanent" of "members" dropped on aligned hyp word "member"
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "China" <-nsubj-- "pushing vs. hyp "China" <-nsubj-- "member", which aligned to text "members" args have different parents, different relations: text "U.N._Security_Council" <-nsubj-- "lift" vs. hyp "U.N._Security_Council" <-prep_of-- "member", which aligned to text "members"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.6874
Threshold: -1.8794


Inference ID: 1601

Txt: Mr Green, Marks & Spencer's new owner, has made a lot of money by taking over underperforming businesses.

Hyp: Mr Green tries to take over Marks & Spencer. (don't know)

Mr_Green
NNP
tries
VBZ
to
TO
take
VB
Marks_&_Spencer
NNP
Mr_Green:NNP   0.00 15.50 20.50 15.50   6.83
Marks_&_Spencer:NNP   6.83 15.50 20.50 15.50   0.00
new:JJ 12.50 12.00 20.00 11.34 12.50
owner:NN   6.21 14.09 20.00 15.00   3.43
has:VBZ 15.50 10.00 20.00 10.00 15.50
made:VBN 15.50   8.18 20.00   9.93 15.50
a:DT 20.50 20.00 10.00 20.00 20.50
lot:NN   7.43 14.82 20.00 13.77   8.78
money:NN   7.06 14.21 20.00 13.47   8.52
by:IN 20.50 20.00 20.00 20.00 20.50
taking_over:VBG 15.50 10.00 20.00 10.00 15.50
underperforming:VBG 15.50   8.33 20.00   8.14 15.50
businesses:NNS   6.20 13.13 20.00 14.89   7.89
NO_WORD 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.98 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "owner" of "Mr_Green" dropped on aligned hyp word "Mr_Green"
-1.00  1.00 NullPunisher.other : take
-1.00  1.00 NullPunisher.other : tries
-0.10  1.00 NullPunisher.functionWord : to
-2.00  1.00 RootEntailment.unalignedRoot : "tries" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.4057
Threshold: -1.8794


Inference ID: 1467

Txt: In an address to the IAEA board of governors in Vienna, Hans Blix said a technical team that visited North Korea last month found that "facilities subject to the freeze were not in operation and that construction work had stopped."

Hyp: Hans Blix is the director general of IAEA. (don't know)

Hans_Blix
NNP
is
VBZ
the
DT
director
NN
general
NN
IAEA
NNP
an:DT 20.50 20.00 10.00 20.00 20.00 20.50
address:NN 10.20 15.00 20.00   8.77   8.55 10.50
the:DT 20.50 20.00   0.00 20.00 20.00 20.50
IAEA:NNP 10.50 15.50 20.50 10.50 10.50   0.00
board:NN   8.50 15.00 20.00   7.15   7.59 10.50
governors:NNS 10.10 15.00 20.00   5.35   7.47 10.50
Vienna:NNP 10.28 15.50 20.50   9.11   9.08 10.50
Hans_Blix:NNP   0.00 15.50 20.50   9.58   9.55 10.50
said:VBD 15.50 10.00 20.00 15.00 14.34 15.50
a:DT 20.50 20.00 10.00 20.00 20.00 20.50
technical:JJ 12.50 12.00 20.00 10.20 12.00 12.50
team:NN   8.51 15.00 20.00   7.67   7.62 10.50
that:WDT 20.50 20.00 10.00 20.00 20.00 20.50
visited:VBD 15.50 10.00 20.00 15.00 15.00 15.50
North_Korea:NNP 10.50 15.50 20.50 10.50 10.50 10.50
last:JJ 12.50 12.00 20.00 12.00 12.00 12.50
month:NN   9.19 15.00 20.00   6.83   6.77 10.50
found:VBD 15.50 10.00 20.00 15.00 15.00 15.50
that:IN 20.50 20.00 20.00 20.00 20.00 20.50
facilities:NNS   9.20 15.00 20.00   6.02   5.96 10.50
subject:JJ 12.50 12.00 20.00 12.00 10.41 12.50
the:DT 20.50 20.00   0.00 20.00 20.00 20.50
freeze:NN 10.27 15.00 20.00   8.92   8.89 10.50
were:VBD 15.50   0.00 20.00 15.00 15.00 15.50
not:RB 15.50 20.00 20.00 15.00 15.00 15.50
in_operation:IN 20.50 20.00 20.00 20.00 20.00 20.50
that:IN 20.50 20.00 20.00 20.00 20.00 20.50
construction:NN   9.71 15.00 20.00   7.76   7.72 10.50
work:NN   8.90 15.00 20.00   6.35   6.28 10.50
had:VBD 15.50 10.00 20.00 15.00 15.00 15.50
stopped:VBN 15.50 10.00 20.00 15.00 15.00 15.50
NO_WORD 10.00   1.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.08 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Vienna" of "governors" dropped on aligned hyp word "director"
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "general" aligned badly to "board"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Hans_Blix" <-nsubj-- "said" vs. hyp "Hans_Blix" <-nsubj-- "general", which aligned to text "board" args have different parents, different relations: text "IAEA" <-prep_to-- "address" vs. hyp "IAEA" <-prep_of-- "general", which aligned to text "board"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.7184
Threshold: -1.8794


Inference ID: 944

Txt: The U.S. handed power on June 30 to Iraq's interim government chosen by the United Nations and Paul Bremer, former governor of Iraq.

Hyp: The U.S. chose Paul Bremer as new governor of Iraq. (don't know)

The
DT
U.S.
NNP
chose
VBD
Paul_Bremer
NNP
new
JJ
governor
NN
Iraq
NNP
The:DT   0.00 20.50 20.00 20.50 20.00 20.00 20.50
U.S.:NNP 20.50   0.00 15.50   8.49 12.50   8.83   8.47
handed:VBD 20.00 15.50   6.37 15.50 11.86 14.67 15.50
power:NN 20.00   7.25 13.29   8.34 11.03   8.19   9.32
June:NNP 20.50   8.35 15.50   9.18 12.50   9.44   9.88
30:CD 20.50 20.50 20.50 20.50 20.50 20.49 20.50
Iraq:NNP 20.50   8.47 15.50   9.72 12.50   9.84   0.00
interim:JJ 20.00 12.50 11.60 12.50 10.00 12.00 12.50
government:NN 20.00   1.32 14.98   8.15 12.00   5.00   8.90
chosen:VBN 20.00 15.50   0.00 15.50 11.77 13.62 15.50
the:DT   0.00 20.50 20.00 20.50 20.00 20.00 20.50
United_Nations:NNP 20.50   7.70 15.50   9.75 12.50   9.93 10.22
Paul_Bremer:NNP 20.50   8.49 15.50   0.00 12.50   8.72   9.72
former:JJ 20.00 12.50 12.00 12.50   8.84 12.00 12.50
governor:NN 20.00   8.83 13.37   8.72 10.30   0.00   9.84
Iraq:NNP 20.50   8.47 15.50   9.72 12.50   9.84   0.00
NO_WORD   1.00 10.00 10.00 10.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.50 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  0.57 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "United_Nations" of "chosen" dropped on aligned hyp word "chose"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "U.S." <-nsubj-- "handed" vs. hyp "U.S." <-nsubj-- "chose", which aligned to text "chosen" args have different parents, different relations: text "governor" <-dobj-- "handed" vs. hyp "governor" <-prep_as-- "chose", which aligned to text "chosen" text "Paul_Bremer" is prep_by of "chosen" while hyp "Paul_Bremer" is dobj of "chose" which aligned to text "chosen"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.8140
Threshold: -1.8794


Inference ID: 2085

Txt: The first Windows DNA lab outside Microsoft was established in suburban Philadelphia in June 1998.

Hyp: Microsoft was established in June 1998. (don't know)

Microsoft
NNP
was
VBD
established
VBN
June
NNP
1998
CD
The:DT 20.50 20.00 20.00 20.50 20.50
first:JJ 12.50 12.00 12.00 12.50 20.50
Windows:NNP 10.50 15.00 15.00   9.46 20.50
DNA:NNP 10.50 15.00 15.00   9.79 20.50
lab:NN 10.50 15.00 15.00   9.37 20.50
Microsoft:NNP   0.00 15.50 15.50 10.50 20.50
was:VBD 15.50   0.00 10.00 15.50 20.50
established:VBN 15.50 10.00   0.00 15.50 20.50
suburban:JJ 12.50 12.00 11.63 12.50 20.50
Philadelphia:NNP 10.50 15.50 15.50   9.48 20.50
June:NNP 10.50 15.50 15.50   0.00 20.00
1998:CD 20.50 20.50 20.50 20.00   0.00
NO_WORD 10.00   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.83 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Philadelphia" of "established" dropped on aligned hyp word "established"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 06/01/1998
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Microsoft" <-prep_outside-- "lab" vs. hyp "Microsoft" <-nsubjpass-- "established", which aligned to text "established"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.1656
Threshold: -1.8794


Inference ID: 1926

Txt: Ahern, who was travelling to Tokyo for an EU-Japan summit yesterday, will consult with other EU leaders about the election of the EU commission president.

Hyp: EU leaders take part in an EU-Japan summit. (don't know)

EU
NNP
leaders
NNS
take
VBP
part
NN
an
DT
EU-Japan
JJ
summit
NN
Ahern:NNP 10.50 10.50 15.50 10.50 20.50 12.50 10.50
who:WP 12.50 12.00 15.00 12.00 20.00 15.00 12.00
was:VBD 15.50 15.00 10.00 15.00 20.00 12.00 15.00
traveling:VBG 15.50 13.55 10.00 15.00 20.00 12.00 11.72
Tokyo:NNP 10.50   9.03 15.50   7.94 20.50 12.50   9.19
an:DT 20.50 18.79 20.00 20.00   0.00 20.00 20.00
EU-Japan:JJ 12.50 12.00 12.00 12.00 20.00   0.00 12.00
summit:NN   9.66   6.28 13.97   7.57 20.00 12.00   0.00
yesterday:NN 10.50   8.08 15.00   5.86 20.00 12.00   8.26
will:MD 20.50 20.00 20.00 20.00 10.00 20.00 20.00
consult:VB 15.50 13.16   7.68 15.00 20.00 12.00 12.12
other:JJ 12.50 12.00 12.00 12.00 20.00 10.00 12.00
EU:NNP   0.00 10.50 15.50 10.50 20.50 12.50   9.66
leaders:NNS 10.50   0.00 14.78   7.34 18.79 12.00   6.28
the:DT 20.50 20.00 20.00 20.00 10.00 20.00 20.00
election:NN 10.50   7.07 13.64   7.54 20.00 12.00   8.37
the:DT 20.50 20.00 20.00 20.00 10.00 20.00 20.00
EU:NNP   0.50 10.50 15.50 10.50 20.50 12.50   9.66
commission:NN   9.45   6.38 15.00   6.51 20.00 12.00   8.05
president:NN 10.50   7.90 13.90   6.57 20.00 12.00   5.93
NO_WORD 10.00 10.00 10.00 10.00   1.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.45 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.43 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "other" of "leaders" dropped on aligned hyp word "leaders"
-1.00  1.00 NullPunisher.other : take
-1.00  1.00 NullPunisher.other : part
 1.00  1.00 Quant.contract : [an,an]
-2.00  1.00 RootEntailment.unalignedRoot : "take" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.5174
Threshold: -1.8794


Inference ID: 1908

Txt: Romano Prodi will meet the US President George Bush in his capacity as president of the European commission.

Hyp: Romani Prodi is the US President. (don't know)

Romani_Prodi
NNP
is
VBZ
the
DT
US
NNP
President
NNP
Romano_Prodi:NNP   3.33 15.50 20.50 10.50 10.50
will:MD 20.50 20.00 10.00 20.50 20.00
meet:VB 15.50 10.00 20.00 15.50 15.00
the:DT 20.50 20.00   0.00 20.50 20.00
US:NNP 10.50 15.50 20.50   0.00   6.79
President:NNP 10.50 15.00 20.00   6.79   0.00
George_Bush:NNP 10.00 15.50 20.50   7.37   6.91
his:PRP$ 12.50 13.00 20.00 12.50 12.00
capacity:NN 10.50 15.00 20.00   8.44   8.18
president:NN 10.50 15.00 20.00   6.79   0.00
the:DT 20.50 20.00   0.00 20.50 20.00
European:JJ 12.50 12.00 20.00 12.50 12.00
commission:NN 10.50 15.00 20.00   7.36   7.16
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.16 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "capacity" of "George_Bush" dropped on aligned hyp word "Romani_Prodi"
-1.00  1.00 Apposition.mismatch : no apposition in text between President and Romani_Prodi
-0.10  1.00 NullPunisher.article : the
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 Person.mismatch : person mimatch between Romani_Prodi and George_Bush
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "US" <-dobj-- "meet" vs. hyp "US" <-nn-- "President", which aligned to text "President"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -5.0329
Threshold: -1.8794


Inference ID: 1659

Txt: The Anzio beachhead, 33 miles south of Rome, saw some of the bloodiest fighting of World War II, and accounted for many of the 10,000 US battle deaths in Italy in the first half of 1944.

Hyp: The Anzio beachhead is located 33 miles south of Rome. (yes)

The
DT
Anzio
NNP
beachhead
NN
is
VBZ
located
VBN
33
CD
miles
NNS
south
RB
Rome
NNP
The:DT   0.00 20.50 20.00 20.00 20.00 20.50 20.00 20.00 20.50
Anzio:NNP 20.50   0.00 10.50 15.50 15.50 20.50 10.50 15.50 10.00
beachhead:NN 20.00 10.50   0.00 15.00 10.26 20.37   7.79 10.40 10.50
33:CD 20.50 20.50 20.37 20.50 20.50   0.00 20.50 20.28 20.50
miles:NNS 20.00 10.50   7.79 15.00   7.67 20.50   0.00   9.19   9.91
south:RB 20.00 15.50 10.40 20.00 13.19 20.28   9.19   0.00 15.50
Rome:NNP 20.50 10.00 10.50 15.50 15.50 20.50   9.91 15.50   0.00
saw:VBD 20.00 15.50 15.00 10.00 10.00 18.43 15.00 19.39 15.50
some:DT 10.00 20.50 20.00 20.00 20.00 20.50 20.00 20.00 15.50
the:DT   0.00 20.50 20.00 20.00 20.00 20.50 20.00 20.00 20.50
bloodiest:JJ 20.00 12.50 10.60 12.00 12.00 19.95 10.29 11.31 12.50
fighting:NN 20.00 10.50   9.35 15.00 15.00 19.57   8.54 13.28   9.70
World_War_II:NNP 20.00 10.50 10.00 15.00 15.00 20.50 10.00 15.00 10.50
accounted:VBD 20.00 15.50 14.34 10.00   9.15 18.45 14.93 19.66 15.50
many:JJ 20.00 12.50 12.00 12.00 12.00 20.50 12.00 12.00 12.50
the:DT   0.00 20.50 20.00 20.00 20.00 20.50 20.00 20.00 20.50
10,000:CD 20.50 20.50 20.50 20.50 20.50   5.00 19.90 20.50 20.50
US:NNP 20.50 10.00 10.50 15.50 15.50 20.50   8.49 15.50   6.05
battle:NN 20.00 10.50   6.81 15.00 14.84 19.85   8.67 12.63   9.69
deaths:NNS 20.00 10.50 10.00 15.00 14.89 20.50   7.41 13.98   9.50
Italy:NNP 20.50 10.00 10.50 15.50 15.50 20.50   9.57 15.50   6.85
the:DT   0.00 20.50 20.00 20.00 20.00 20.50 20.00 20.00 20.50
first_half:NN 20.00 10.50 10.00 15.00 15.00 20.50   7.98 15.00 10.35
1944:CD 20.50 20.50 20.50 20.50 20.37 10.50 19.66 18.98 20.50
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.59 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.78 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : located
-2.00  1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
Hand-tuned score (dot product of above): -0.2532
Threshold: -1.8794


Inference ID: 1950

Txt: Crude oil dips below $43 on news that Russia's justice ministry will not force Yukos to halt sales.

Hyp: Crude oil rises. (don't know)

Crude_oil
NN
rises
VBZ
Crude_oil:NN   0.00 13.55
dips:VBD 15.00   6.11
$:$ 20.50 20.50
43:CD 20.50 20.50
news:NN   8.45 14.30
that:IN 20.00 20.00
Russia:NNP   9.44 15.50
justice:NN   8.35 15.00
ministry:NN   8.90 15.00
will:MD 20.00 20.00
not:RB 15.00 20.00
force:VB 15.00   8.44
Yukos:NNP 10.50 15.50
to:TO 20.00 20.00
halt:VB 15.00 10.00
sales:NNS   8.17 15.00
NO_WORD 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.82 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : rises
-2.00  1.00 RootEntailment.unalignedRoot : "rises" not aligned to anything
Hand-tuned score (dot product of above): -1.2724
Threshold: -1.8794


Inference ID: 102

Txt: The White House failed to act on the domestic threat from al Qaeda prior to September 11, 2001.

Hyp: White House ignored the threat of attack (yes)

White_House
NNP
ignored
VBD
the
DT
threat
NN
attack
NN
The:DT 20.00 20.00   0.00 20.00 20.00
White_House:NNP   0.50 15.50 20.50 10.33 10.10
failed:VBD 15.00   6.27 20.00 12.67 11.71
to:TO 20.00 20.00 10.00 20.00 20.00
act:VB 15.00   7.87 20.00 11.97 15.00
the:DT 20.00 20.00   0.00 20.00 20.00
domestic:JJ 12.00 10.96 20.00   9.84 12.00
threat:NN   9.83 10.27 20.00   0.00   5.74
al:NNP   9.87 15.00 20.00   8.04   8.62
Qaeda:NNP   8.91 15.50 20.50   9.41   8.82
September_11:NNP 10.13 15.50 20.50   9.03   8.34
2001:CD 20.50 20.50 20.50 20.50 20.50
NO_WORD 10.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.84 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added attack[attack-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Qaeda" of "threat" dropped on aligned hyp word "threat"
-1.00  1.00 NullPunisher.other : ignored
-1.00  1.00 NullPunisher.other : attack
-2.00  1.00 RootEntailment.unalignedRoot : "ignored" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.2569
Threshold: -1.8794


Inference ID: 2141

Txt: No Weapons of Mass Destruction Found in Iraq Yet.

Hyp: Weapons of Mass Destruction Found in Iraq. (don't know)

Weapons
NNP
Mass
NNP
Destruction
NNP
Found
NNP
Iraq
NNP
No:DT 20.00 20.00 20.00 20.00 20.50
Weapons:NNP   0.00   8.20   8.17 10.00   9.29
Mass:NNP   8.20   0.00   8.69 10.00   9.93
Destruction:NNP   8.17   8.69   0.00 10.00   9.91
Found:NNP 10.00 10.00 10.00   0.00 10.50
Iraq:NNP   9.29   9.93   9.91 10.50   0.00
Yet:RB 15.00 15.00 15.00 15.00 15.50
NO_WORD 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.56 Alignment.score
 1.00  0.97 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Yet" of "Weapons" dropped on aligned hyp word "Weapons"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Iraq" <-prep_in-- "Found" vs. hyp "Iraq" <-prep_in-- "Weapons", which aligned to text "Weapons"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.8649
Threshold: -1.8794


Inference ID: 100

Txt: A priest who served in the Diocese of Metuchen in the mid-1980s has been sentenced to life in prison for sexually abusing a Massachusetts boy.

Hyp: Pedophile gets life in prison (yes)

Pedophile
RB
gets
VBZ
life
NN
prison
NN
A:DT 20.00 20.00 20.00 20.00
priest:NN 15.00 15.00   6.82   6.51
who:WP 20.00 15.00 12.00 12.00
served:VBD 20.00 10.00 14.36 10.84
the:DT 20.00 20.00 20.00 20.00
Diocese:NNP 15.00 15.00 10.00 10.00
Metuchen:NNP 15.00 15.00 10.00 10.00
the:DT 20.00 20.00 20.00 20.00
mid-1980s:NNS 15.00 15.00   8.16   8.42
has:VBZ 20.00 10.00 15.00 15.00
been:VBN 20.00 10.00 15.00 15.00
sentenced:VBN 20.00 10.00 12.25   6.40
life:NN 15.00 14.27   0.00   6.51
prison:NN 15.00 15.00   6.51   0.00
for:IN 20.00 20.00 20.00 20.00
sexually:RB 10.00 20.00 14.04 12.01
abusing:VBG 20.00   8.51 13.86 12.08
a:DT 20.00 20.00 20.00 20.00
Massachusetts:NNP 15.50 15.50   9.31   9.17
boy:NN 15.00 14.16   6.27   6.96
NO_WORD   9.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.88 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Pedophile[Pedophile-RB]
-1.00  1.00 NullPunisher.other : Pedophile
-1.00  1.00 NullPunisher.other : gets
-2.00  1.00 RootEntailment.unalignedRoot : "gets" not aligned to anything
Hand-tuned score (dot product of above): -3.2007
Threshold: -1.8794


Inference ID: 256

Txt: Eilat, Israel's most southerly port and the country's only access to the Red Sea, has long been a winter destination for tourists, 80% of whom are Europeans attracted by its temperate climates and clear waters.

Hyp: In winter most tourists to Eilat come from Europe. (yes)

winter
NN
most
JJS
tourists
NNS
Eilat
NNP
come
VBP
Europe
NNP
Eilat:NNP 10.50 12.50 10.50   0.50 15.50 10.50
Israel:NNP   9.78 12.50   9.47 10.50 15.50   8.51
most:JJS 12.00   0.00 12.00 12.50 12.00 12.50
southerly:NN   7.72 12.00   7.51 10.50 14.57 10.50
port:NN   7.98 12.00   5.45 10.50 14.09   8.07
the:DT 20.00 20.00 20.00 20.50 20.00 20.50
country:NN   7.95 12.00   6.25 10.50 13.53   7.19
only:JJ 12.00   8.39 12.00 12.50 12.00 12.50
access:NN   9.06 12.00   8.97 10.50 15.00   9.13
the:DT 20.00 20.00 20.00 20.50 20.00 20.50
Red_Sea:NNP 10.50 12.50 10.50 10.00 15.50 10.50
has:VBZ 15.00 12.00 15.00 15.50 10.00 15.50
long:RB 12.53 12.00 15.00 15.50 14.91 15.50
been:VBN 15.00 12.00 15.00 15.50 10.00 15.50
a:DT 20.00 20.00 20.00 20.50 20.00 20.50
winter:NN   0.00 12.00   7.91 10.50 12.32   9.45
destination:NN   7.32 12.00   3.97 10.50 13.81   8.64
tourists:NNS   7.91 12.00   0.00 10.50 13.78   8.95
80:CD 20.18 20.50 19.32 20.50 19.20 20.50
%:NN 10.50 12.50   9.81 10.50 15.50 10.50
of:IN 20.00 20.00 20.00 20.50 20.00 20.50
whom:WP 12.00 15.00 12.00 12.50 15.00 12.50
are:VBP 15.00 12.00 15.00 15.50 10.00 15.50
Europeans:NNPS   8.84 12.00   7.87 10.50 15.00   5.50
attracted:VBN 14.56 12.00 13.58 15.50   9.20 15.50
its:PRP$ 12.00 13.47 12.00 12.50 15.00 12.50
temperate:JJ   9.66 10.00 10.31 12.50 10.41 12.50
climates:NNS   8.23 12.00   6.36 10.50 13.84   9.54
clear:JJ 12.00 10.00 12.00 12.50 11.22 12.50
waters:NNS   5.61 12.00   6.18 10.50 13.13   9.31
NO_WORD 10.00   9.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.09 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "attracted" of "Europeans" dropped on aligned hyp word "Europe"
-2.00  1.00 Location.mismatch : no clear info of matching: come(X, prep_from)
 0.00  1.00 NegPolarity.hypNegWord : "most": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord : "most": tag "JJS" is in NegPolarityMarkers list
-1.00  1.00 NullPunisher.other : come
-2.00  1.00 RootEntailment.unalignedRoot : "come" not aligned to anything
-1.00  1.00 Structure.relMismatch : noun args have different parents but same relations: "most": "port" vs. "tourists"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
 0.00  1.00 NegPolarity.txtNegWord&NegPolarity.hypNegWord :
Hand-tuned score (dot product of above): -2.7019
Threshold: -1.8794


Inference ID: 72

Txt: On the eve of a widely anticipated retaliation against Russian forces in Grozny, military commanders withdrew key units from the city, leaving it vulnerable to guerrilla infiltration.

Hyp: Russian forces vulnerable to guerrilla fighters (don't know)

Russian
JJ
forces
NNS
vulnerable
JJ
guerrilla
NN
fighters
NNS
the:DT 20.00 20.00 20.00 20.00 20.00
eve:NN 12.00   8.72 11.89   8.58   8.42
a:DT 20.00 20.00 20.00 20.00 20.00
widely:RB 12.00 15.00 10.56 14.16 15.00
anticipated:VBN 12.00 15.00 10.07 15.00 15.00
retaliation:NN 12.00   4.51 11.35   7.50   6.74
Russian:NNP   0.50   8.78 12.50   8.97   8.78
forces:NNS 12.00   0.00   8.88   3.73   2.66
Grozny:NNP 12.50 10.50 12.50 10.50 10.50
military:JJ   7.84   3.11   8.84   6.64   4.49
commanders:NNS 12.00   2.08 11.47   4.10   2.05
withdrew:VBD 12.00 13.52 12.00 13.25 13.04
key:JJ 10.00 10.50   7.42 12.00 12.00
units:NNS 12.00   5.63 12.00   8.01   7.27
the:DT 20.00 20.00 20.00 20.00 20.00
city:NN 12.00   6.02 12.00   7.86   7.01
leaving:VBG 12.00 15.00 10.18 15.00 15.00
it:PRP 15.00 12.00 15.00 12.00 12.00
vulnerable:JJ 10.00   8.88   0.00 10.82 11.67
guerrilla:NN 12.00   3.73 10.82   0.00   4.41
infiltration:NN 12.00   5.17   9.55   4.13   7.48
NO_WORD   9.00 10.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.35 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Grozny" of "forces" dropped on aligned hyp word "forces"
-1.00  1.00 Structure.relMismatch : noun args have different parents but same relations: "vulnerable": "leaving" vs. "forces"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.8257
Threshold: -1.8794


Inference ID: 2142

Txt: No stockpiles of weapons of mass destruction have been found in Iraq since Saddam's regime was toppled in a US-led invasion last year.

Hyp: Weapons of mass destruction found in Iraq. (don't know)

Weapons
NNP
mass
JJ
destruction
NN
found
VBD
Iraq
NNP
No:DT 20.00 20.00 20.00 20.00 20.50
stockpiles:NNS   7.47 10.54   8.07 14.81   9.53
weapons_of_mass_destruction:NP 20.00 20.00 15.00 20.00 20.50
have:VBP 15.00 12.00 15.00   7.71 15.50
been:VBN 15.00 12.00 15.00 10.00 15.50
found:VBN 15.00 10.48 13.65   0.00 15.50
Iraq:NNP   9.29 12.50   9.91 15.50   0.00
since:IN 20.00 20.00 20.00 20.00 20.50
Saddam:NNP 10.50 12.50 10.50 15.50   8.98
regime:NN   6.88   9.40   4.75 15.00   9.19
was:VBD 15.00 12.00 15.00 10.00 15.50
toppled:VBN 15.00   9.54 12.95   9.97 15.50
a:DT 20.00 20.00 20.00 20.00 20.50
US-led:JJ 12.00   9.79 11.34 10.79 12.50
invasion:NN   9.15   8.89   5.03 14.89 10.34
last:JJ 12.00 10.00 12.00 12.00 12.50
year:NN   7.12 11.74   7.77 14.37   9.33
NO_WORD 10.00   9.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.77 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added mass[mass-JJ]
-1.00  1.00 NullPunisher.other : mass
-1.00  1.00 Structure.relMismatch : text "stockpiles" is nsubjpass of "found" while hyp "Weapons" is nsubj of "found" which aligned to text "found"
Hand-tuned score (dot product of above): -1.1325
Threshold: -1.8794


Inference ID: 258

Txt: The Scots pioneered the idea of a single currency, when they shared the Ecu with France during the Auld Alliance, and therefore they are certain to join in the monetary union process again when the EU states amalgamate their currencies under Maastricht.

Hyp: France still maintains Ecu is the best denomination for the Maastricht single-currency.. (don't know)

France
NNP
still
RB
maintains
VBZ
Ecu
NNP
is
VBZ
the
DT
best
JJS
denomination
NN
the
DT
Maastricht
NNP
single-currency
NN
.
NNP
The:DT 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00
Scots:NNPS   9.91 15.00 15.00 10.50 15.00 20.00 12.00   9.68 20.00 10.00   9.70 10.00
pioneered:VBD 15.50 20.00   7.17 15.50 10.00 20.00 12.00 15.00 20.00 15.00 14.20 15.00
the:DT 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00
idea:NN   7.61 15.00 12.97 10.50 15.00 20.00   9.24   7.88 20.00 10.00   7.95   7.71
a:DT 20.50 20.00 20.00 20.50 20.00 10.00 20.00 20.00 10.00 20.00 20.00 20.00
single:JJ 12.50 12.00 10.90 12.50 12.00 20.00   9.41 11.73 20.00 12.00   5.91 11.16
currency:NN   8.13 15.00 15.00 10.50 15.00 20.00 12.00   7.89 20.00 10.00   0.00   9.95
when:WRB 20.50 20.00 20.00 20.50 20.00 10.00 20.00 20.00 10.00 20.00 20.00 20.00
they:PRP 12.50 20.00 15.00 12.50 15.00 15.71 15.00 12.00 15.71 12.00 12.00 12.00
shared:VBD 15.50 20.00   6.35 15.50 10.00 20.00 10.16 13.06 20.00 15.00 14.65 13.62
the:DT 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00
Ecu:NN 10.50 15.00 15.00   0.50 15.00 20.00 12.00 10.00 20.00 10.00 10.00 10.00
France:NNP   0.00 15.50 15.50 10.50 15.50 20.50 12.50   9.75 20.50 10.50   9.79 10.50
the:DT 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00
Auld_Alliance:NNP   9.46 15.50 15.50 10.50 15.50 20.50 12.50   9.86 20.50 10.50   9.89 10.50
therefore:RB 15.50 10.00 20.00 15.50 20.00 20.00 12.00 15.00 20.00 15.00 15.00 15.00
they:PRP 12.50 20.00 15.00 12.50 15.00 15.71 15.00 12.00 15.71 12.00 12.00 12.00
are:VBP 15.50 20.00 10.00 15.50   0.00 20.00 12.00 15.00 20.00 15.00 15.00 15.00
certain:JJ 12.50 12.00 10.67 12.50 12.00 20.00   7.52 12.00 20.00 12.00 11.91   8.90
to:TO 20.50 20.00 20.00 20.50 20.00 10.00 20.00 20.00 10.00 20.00 20.00 20.00
join:VB 15.50 20.00   9.94 15.50 10.00 20.00 12.00 13.68 20.00 15.00 12.48 15.00
the:DT 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00
monetary:JJ 12.50 12.00 12.00 12.50 12.00 20.00 10.00   9.89 20.00 12.00   7.35 12.00
union:NN   9.00 15.00 15.00 10.50 15.00 20.00 12.00   7.51 20.00 10.00   8.19   9.66
process:NN   8.52 15.00 14.81 10.50 15.00 20.00 11.51   8.62 20.00 10.00   7.86   7.48
again:RB 15.50 10.00 20.00 15.50 20.00 20.00 12.00 15.00 20.00 15.00 15.00 15.00
when:WRB 20.50 20.00 20.00 20.50 20.00 10.00 20.00 20.00 10.00 20.00 20.00 20.00
the:DT 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00   0.00 20.00 20.00 20.00
EU:NNP   8.66 15.50 15.50 10.50 15.50 20.50 12.50 10.50 20.50 10.50 10.50 10.50
states:NNS   8.74 15.00 13.94 10.50 15.00 20.00 12.00   7.37 20.00 10.00   8.85   9.43
amalgamate:VB 15.50 20.00 10.00 15.50 10.00 20.00 12.00 15.00 20.00 15.00 15.00 15.00
their:PRP$ 12.50 20.00 15.00 12.50 15.00 20.00 15.00 12.00 20.00 12.00 12.00 12.00
currencies:NNS   8.13 15.00 15.00 10.50 15.00 20.00 12.00   8.31 20.00 10.00   0.00   9.56
Maastricht:NNP 10.50 15.50 15.50 10.50 15.50 20.50 12.50 10.50 20.50   0.50 10.50 10.50
NO_WORD 10.00   9.00 10.00 10.00   1.00   1.00   9.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.67 Alignment.score
 1.00  0.84 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  8.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added best[best-JJS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "single" of "currency" dropped on aligned hyp word "single-currency"
 0.00  1.00 NegPolarity.hypNegWord : "best": tag "JJS" is in NegPolarityMarkers list
-1.00  1.00 NullPunisher.other : best
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : .
-1.00  1.00 NullPunisher.other : maintains
-1.00  1.00 NullPunisher.other : still
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : denomination
 1.00  1.00 Quant.contract : [a,the]
-2.00  1.00 RootEntailment.unalignedRoot : "." not aligned to anything
-2.00  1.00 RootEntailment.unalignedRoot : "maintains" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -7.3218
Threshold: -1.8794


Inference ID: 128

Txt: Hippos do come into conflict with people quite often.

Hyp: Hippopotamus attacks human. (yes)

Hippopotamus
NNS
attacks
VBZ
human
JJ
Hippos:NNP   5.50 15.50 12.50
do:VBP 15.00 10.00 11.25
come:VB 15.00 10.00 12.00
conflict:NN 10.00 12.56   8.99
people:NNS 10.00 13.38 10.13
quite:RB 15.00 20.00 10.49
often:RB 15.00 20.00 12.00
NO_WORD 10.00 10.00   9.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.11 Alignment.score
 1.00  0.71 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : attacks
-1.00  1.00 NullPunisher.other : human
-2.00  1.00 RootEntailment.unalignedRoot : "attacks" not aligned to anything
Hand-tuned score (dot product of above): -3.4698
Threshold: -1.8794


Inference ID: 137

Txt: Acid attacks on women are carried out across Asia, with reports of such incidents from Burma, Cambodia, India and Pakistan.

Hyp: Acid attacks are carried out on women (yes)

Acid
VBN
attacks
NNS
are
VBP
carried_out
VBN
women
NNS
Acid:VBN   0.00 15.00 10.00 10.00 15.00
attacks:NNS 15.00   0.00 15.00 15.00   7.57
women:NNS 15.00   7.57 15.00 15.00   0.00
are:VBP 10.00 15.00   0.00 10.00 15.00
carried_out:VBN 10.00 15.00 10.00   0.00 15.00
Asia:NNP 15.50   8.63 15.50 15.50   7.82
reports:NNS 15.00   7.79 15.00 15.00   7.61
such:JJ 12.00 12.00 12.00 12.00 12.00
incidents:NNS 15.00   5.29 15.00 15.00   7.85
Burma:NNP 15.50 10.50 15.50 15.50 10.50
Cambodia:NNP 15.50   9.47 15.50 15.50   9.01
India:NNP 15.50   9.14 15.50 15.50   8.60
Pakistan:NNP 15.50   9.49 15.50 15.50   9.02
NO_WORD 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.76 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "reports" of "carried_out" dropped on aligned hyp word "carried_out"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "women" <-prep_on-- "attacks" vs. hyp "women" <-prep_on-- "carried_out", which aligned to text "carried_out"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.7029
Threshold: -1.8794


Inference ID: 24

Txt: Dr. Amy Joy Lanou, director of the Physicians Committee for Responsible Medicine in America, was quick to point out health dangers of the Atkins diet.

Hyp: Atkins diet is associated with increased risk conditions. (yes)

Atkins
NNP
diet
NN
is
VBZ
associated
VBN
increased
VBN
risk
NN
conditions
NNS
Dr._Amy_Joy_Lanou:NNP 10.50   9.52 15.50 15.50 15.50   8.77   7.25
director:NN 10.00   7.56 15.00 14.18 14.59   6.84   6.37
the:DT 20.00 20.00 20.00 20.00 20.00 20.00 20.00
Physicians_Committee_for_Responsible_Medicine:NNP 10.50   8.81 15.50 15.50 15.50   7.09   6.59
America:NNP 10.50   8.64 15.50 15.50 15.50   7.58   6.31
was:VBD 15.00 15.00   0.00 10.00 10.00 15.00 15.00
quick:JJ 12.00 11.26 12.00 11.69 12.00   8.97 11.08
to:TO 20.00 20.00 20.00 20.00 20.00 20.00 20.00
point_out:VB 15.00 15.00 10.00 10.00 10.00 15.00 15.00
health:NN 10.00   5.56 15.00 12.16 14.78   7.13   4.85
dangers:NNS 10.00   6.53 15.00 12.70 14.86   5.78   3.92
the:DT 20.00 20.00 20.00 20.00 20.00 20.00 20.00
Atkins:NNP   0.50 10.50 15.50 15.50 15.50 10.50 10.50
diet:NN 10.00   0.00 15.00 12.34 14.61   6.06   8.06
NO_WORD 10.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.87 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added conditions[conditions-NNS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "health" of "dangers" dropped on aligned hyp word "associated"
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : increased
-1.00  1.00 NullPunisher.other : conditions
-1.00  1.00 NullPunisher.other : risk
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "associated" aligned badly to "dangers"
-1.00  1.00 Structure.relMismatch : text "diet" is prep_of of "dangers" while hyp "diet" is nsubjpass of "associated" which aligned to text "dangers"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.4907
Threshold: -1.8794


Inference ID: 2140

Txt: Turkey is unlikely to become involved in, or allow U.S. forces to use Turkish territory in a Middle East war that does not threaten her territory directly.

Hyp: U.S. to use Turkish military bases. (don't know)

U.S.
NNP
to
TO
use
VB
Turkish
NNP
military
JJ
bases
NNS
Turkey:NNP   8.28 20.50 15.50   3.00 12.50   8.76
is:VBZ 15.50 20.00 10.00 15.50 12.00 15.00
unlikely:JJ 12.50 20.00 11.70 12.50 10.00 12.00
to:TO 20.50   0.00 20.00 20.50 20.00 20.00
become:VB 15.50 20.00   7.90 15.50 12.00 15.00
involved:VBN 15.50 20.00   8.20 15.50 10.28 14.51
in:IN 20.50 20.00 20.00 20.50 20.00 20.00
allow:VB 15.50 20.00   5.59 15.50 12.00 13.74
U.S.:NNP   0.00 20.50 15.50   8.75 12.50   7.97
forces:NNS   4.58 20.00 13.64 10.50   3.11   4.96
to:TO 20.50   0.00 20.00 20.50 20.00 20.00
use:VB 15.50 20.00   0.00 15.50 10.00 12.60
Turkish:JJ 12.50 20.00 12.00   0.50 10.00 12.00
territory:NN   6.30 20.00 14.56 10.50   7.29   5.77
a:DT 20.50 10.00 20.00 20.50 20.00 20.00
Middle_East:NNP   8.98 20.50 15.50 10.00 12.50   9.46
war:NN   8.37 20.00 15.00 10.50   7.07   6.52
that:WDT 20.50 10.00 20.00 20.50 20.00 20.00
does:VBZ 15.50 17.95   8.13 15.50 11.96 15.00
not:RB 15.50 20.00 20.00 15.50 12.00 15.00
threaten:VB 15.50 20.00   8.28 15.50   8.47 12.49
her:PRP$ 12.50 20.00 15.00 12.50 15.00 12.00
territory:NN   6.30 20.00 14.56 10.50   7.29   5.77
directly:RB 15.50 20.00 16.05 15.50 10.58 13.31
NO_WORD 10.00 10.00 10.00 10.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.04 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added military[military-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "threaten" of "territory" dropped on aligned hyp word "bases"
-1.00  1.00 NullPunisher.other : military
-0.10  1.00 NullPunisher.functionWord : to
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.8725
Threshold: -1.8794


Inference ID: 286

Txt: Security preparations kept protestors at bay at the recent G8 Summit on Sea Island (USA).

Hyp: Demonstrations against G8 Summit took place at Sea Island bay. (don't know)

Demonstrations
NNS
G8
NNP
Summit
NNP
took_place
VBD
Sea_Island
NNP
bay
NN
Security:NNP   6.97   7.89   6.31 15.00   8.76   7.98
preparations:NNS   6.33   8.66   8.77 15.00   9.44   8.73
kept:VBD 15.00 15.00 15.00 10.00 15.50 15.00
protestors:NNS 10.00 10.00 10.00 15.00 10.50   9.58
at_bay:IN 20.00 20.00 20.00 20.00 20.50 20.00
the:DT 20.00 20.00 20.00 20.00 20.50 20.00
recent:JJ 12.00 12.00 12.00 12.00 12.50 11.50
G8:NNP   7.80   0.00   8.66 15.00   9.34   8.62
Summit:NNP   7.95   8.66   0.00 15.00   9.44   8.73
Sea_Island:NNP   8.68   9.34   9.44 15.50   0.00   4.55
USA:NNP   6.73   7.69   7.85 15.00   8.11   7.27
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.09 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added bay[bay-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "recent" of "Summit" dropped on aligned hyp word "Summit"
-1.00  1.00 NullPunisher.other : took_place
-1.00  1.00 NullPunisher.other : bay
-1.00  1.00 NullPunisher.other : Demonstrations
-2.00  1.00 RootEntailment.unalignedRoot : "took_place" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.0727
Threshold: -1.8794


Inference ID: 36

Txt: Scripps Memorial Hospital Encinitas emergency room doctors and nurses treat two to three injured surfers.

Hyp: Scripps Hospital assists surfing accident victims. (yes)

Scripps_Hospital
NNP
assists
VBZ
surfing
VBG
accident
NN
victims
NNS
Scripps_Memorial_Hospital_Encinitas:NNP   0.00 15.50 15.50   9.97   8.90
emergency_room:NNS   9.77 15.00 15.00   9.39   8.94
doctors:NNS   8.76 15.00 14.33   6.12   5.23
nurses:NNS   9.14 14.69 14.20   5.98   5.37
treat:VBP 15.50 10.00   9.15 13.63 13.22
two:CD 20.50 19.80 20.50 18.64 20.50
three:CD 20.50 19.33 20.50 18.81 20.50
injured:JJ 12.50   8.65 11.41   7.70   7.45
surfers:NNS   9.28 15.00   7.52   9.60   8.41
NO_WORD 10.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.12 Alignment.score
 1.00  0.71 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  0.00 Alignment.hypSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added accident[accident-NN]
-3.00  1.00 NullPunisher.entity : Scripps_Hospital
-1.00  1.00 NullPunisher.other : surfing
-1.00  1.00 NullPunisher.other : victims
-1.00  1.00 NullPunisher.other : accident
-1.00  1.00 NullPunisher.other : assists
-2.00  1.00 RootEntailment.unalignedRoot : "assists" not aligned to anything
Hand-tuned score (dot product of above): -9.9189
Threshold: -1.8794


Inference ID: 260

Txt: It would create an underclass of illiterate and impoverished residents, deprived of basic skills, including English-language skills, necessary for the integration of immigrant children into our society and our work force.

Hyp: Language skills are a means of social integration. (yes)

Language
JJ
skills
NNS
are
VBP
a
DT
means
NN
social
JJ
integration
NN
It:PRP 15.00 12.00 15.00 20.00 12.00 15.00 12.00
would:MD 20.00 20.00 20.00 10.00 20.00 20.00 20.00
create:VB 12.00 11.75 10.00 20.00 13.72   8.40 11.02
an:DT 20.00 20.00 20.00   8.73 20.00 20.00 20.00
underclass:NNS 12.00   5.92 15.00 20.00   8.73   6.02   6.63
illiterate:JJ 10.00   9.34 12.00 20.00 11.64   7.13 10.55
impoverished:JJ 10.00 10.03 12.00 20.00 11.91   4.80   9.57
residents:NNS 12.00   8.54 15.00 20.00   8.17 10.42   9.29
deprived:VBN 12.00 15.00 10.00 20.00 12.92   8.53 13.38
basic:JJ 10.00   8.55 12.00 20.00   9.81   5.30   7.51
skills:NNS 12.00   0.00 15.00 20.00   7.78   8.78   6.80
including:VBG 12.00 13.00 10.00 20.00 13.48 10.98 15.00
English-language:JJ   0.00   9.64 12.00 20.00 10.45   7.51   8.09
skills:NNS 12.00   0.00 15.00 20.00   7.78   8.78   6.80
necessary:JJ 10.00   9.73 12.00 20.00   8.12   7.11   9.19
the:DT 20.00 20.00 20.00 10.00 20.00 20.00 20.00
integration:NN 12.00   6.80 15.00 20.00   7.99   8.66   0.00
immigrant:JJ 10.00   8.79 12.00 20.00 12.00   6.20   9.98
children:NNS 12.00   7.42 15.00 20.00   7.93   8.72   9.16
our:PRP$ 15.00 12.00 15.00 20.00 12.00 15.00 12.00
society:NN 12.00   8.51 15.00 20.00   8.14   7.00   8.54
our:PRP$ 15.00 12.00 15.00 20.00 12.00 15.00 12.00
work_force:NN 12.00   7.30 15.00 20.00   6.77 12.00   8.43
NO_WORD   9.00 10.00   1.00   1.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.09 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "children" of "integration" dropped on aligned hyp word "integration"
 1.00  1.00 Hypernym.posWiden : widening in positive context: english-language -> language
-1.00  1.00 NullPunisher.other : means
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : are
-2.00  1.00 RootEntailment.unalignedRoot : "means" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.7611
Threshold: -1.8794


Inference ID: 2143

Txt: There is none. They found as many weapons in this masjid as they found weapons of mass destruction in Iraq.

Hyp: Weapons of mass destruction found in Iraq. (don't know)

Weapons
NNP
mass
JJ
destruction
NN
found
VBD
Iraq
NNP
There:EX 20.00 20.00 20.00 20.00 20.50
is:VBZ 15.00 12.00 15.00 10.00 15.50
none:NN   8.51 12.00   8.94 15.00 10.08
They:PRP 12.00 15.00 12.00 15.00 12.50
found:VBD 15.00 10.48 13.65   0.00 15.50
many:JJ 12.00 10.00 12.00 12.00 12.50
weapons:NNS   0.00   8.77   5.17 14.56   9.29
this:DT 20.00 20.00 20.00 20.00 20.50
masjid:NN 10.00   7.00 10.00 15.00 10.50
as:IN 20.00 20.00 20.00 20.00 20.50
They:PRP 12.00 15.00 12.00 15.00 12.50
found:VBD 15.00 10.48 13.65   0.00 15.50
weapons_of_mass_destruction:NP 20.00 20.00 15.00 20.00 20.50
Iraq:NNP   9.29 12.50   9.91 15.50   0.00
NO_WORD 10.00   9.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.94 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "many" of "weapons" dropped on aligned hyp word "Weapons"
-1.00  1.00 Structure.relMismatch : noun args have different parents but same relations: "mass": "weapons" vs. "destruction" text "weapons" is prep_as of "found" while hyp "Weapons" is nsubj of "found" which aligned to text "found"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.3774
Threshold: -1.8794


Inference ID: 105

Txt: The Security Council voted in 2002 to protect U.S. soldiers and personnel from other nations that haven't ratified the creation of the court through a treaty, and last June renewed the immunity for a year.

Hyp: Immunity for soldiers renewed. (yes)

Immunity
NNP
soldiers
NNS
renewed
VBD
The:DT 20.00 20.00 20.00
Security_Council:NNP   9.85   9.51 15.50
voted:VBD 15.00 15.00   9.72
2002:CD 20.50 20.50 20.38
to:TO 20.00 20.00 20.00
protect:VB 15.00 12.22   8.11
U.S.:NNP   8.69   8.08 15.50
soldiers:NNS   8.66   0.00 13.70
personnel:NNS   7.77   5.48 15.00
other:JJ 12.00 12.00 12.00
nations:NNS   8.15   7.39 13.61
that:WDT 20.00 20.00 20.00
have:VBP 15.00 15.00 10.00
n't:RB 15.00 14.58 20.00
ratified:VBN 15.00 15.00   9.19
the:DT 20.00 20.00 20.00
creation:NN   8.23   7.62 13.73
the:DT 20.00 20.00 20.00
court:NN   8.57   8.03 14.79
a:DT 20.00 20.00 20.00
treaty:NN   8.75   7.85 13.41
last:JJ 12.00 12.00 12.00
June:NNP   9.34   8.86 15.50
renewed:VBD 15.00 13.70   0.00
the:DT 20.00 20.00 20.00
immunity:NN   0.00   8.66 14.62
a:DT 20.00 20.00 20.00
year:NN   8.07   7.44 15.00
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "year" of "renewed" dropped on aligned hyp word "renewed"
-1.00  1.00 Structure.relMismatch : text "immunity" is dobj of "renewed" while hyp "Immunity" is nsubj of "renewed" which aligned to text "renewed"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.6973
Threshold: -1.8794


Inference ID: 279

Txt: The council prohibited any travel outside of Haiti by all officers of the Haitian military and police and all major participants in the September, 1991, coup.

Hyp: The council is prohibited to travel outside of Haiti by military officers. (don't know)

The
DT
council
NN
is
VBZ
prohibited
VBN
to
TO
travel
VB
Haiti
NNP
military_officers
NNS
The:DT   0.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
council:NN 20.00   0.00 15.00 15.00 20.00 13.20 10.00   7.76
prohibited:VBD 20.00 15.00 10.00   0.00 20.00   8.83 15.50 15.00
any:DT 10.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
travel:NN 20.00   7.74 15.00 13.83 20.00   0.00   9.61   6.89
outside:NN 20.00   9.01 15.00 13.71 20.00 11.85   8.48   8.05
Haiti:NNP 20.50 10.00 15.50 15.50 20.50 15.50   0.00   9.32
all:DT 10.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
officers:NNS 20.00   7.55 15.00 13.41 20.00 15.00   9.32   0.00
the:DT   0.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
Haitian:NNP 20.50 10.50 15.50 15.50 20.50 15.50   3.00 10.50
military:NN 20.00   7.03 15.00 14.62 20.00 14.31 10.03   5.00
police:NNS 20.00   4.70 15.00 14.90 20.00 15.00   9.90   7.52
all:DT 10.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
major:JJ 20.00 10.54 12.00 11.12 20.00 11.04 12.50 12.00
participants:NNS 20.00   8.52 15.00 15.00 20.00 15.00   9.82   6.61
the:DT   0.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
September:NNP 20.50   8.70 15.50 15.50 20.50 15.50   9.87   7.95
1991:CD 20.50 20.50 20.50 18.86 20.50 20.50 20.50 20.50
coup:FW 10.00 19.45 20.00 20.00 10.00 20.00 20.50 20.00
NO_WORD   1.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.57 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "military" of "officers" dropped on aligned hyp word "military_officers"
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 Structure.relMismatch : text "council" is nsubj of "prohibited" while hyp "council" is nsubjpass of "prohibited" which aligned to text "prohibited"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.4936
Threshold: -1.8794


Inference ID: 262

Txt: Winning Team John C. Harsanyi, John F. Nash and Reinhard Selten were jointly awarded the Nobel Prize in Economics. The three professors were singled out for their unique contributions to game theory, derived from studies of games such as poker or chess, in which players have to think ahead and devise a strategy based on expected countermoves from opponents.

Hyp: Three scholars shared a Nobel Prize for their studies about the game theory. (yes)

Three
CD
scholars
NNS
shared
VBD
a
DT
Nobel_Prize
NNP
their
PRP$
studies
NNS
the
DT
game_theory
NN
Winning:VBG 20.50 15.00 10.00 20.00 15.00 15.00 15.00 20.00 15.00
Team:NNP 20.50   7.60 15.00 20.00 10.00 12.00   8.12 20.00 10.00
John_C._Harsanyi:NNP 20.50   7.49 15.50 20.50 10.50 12.50   8.69 20.50 10.50
John_F._Nash:NNP 20.50   7.49 15.50 20.50 10.50 12.50   8.69 20.50 10.50
Reinhard_Selten:NNP 20.50 10.50 15.50 20.50 10.50 12.50 10.50 20.50 10.50
were:VBD 20.50 15.00 10.00 20.00 15.00 15.00 15.00 20.00 15.00
jointly:RB 20.50 14.86 18.76 20.00 15.00 15.00 14.64 20.00 15.00
awarded:VBN 20.50 13.92   8.89 20.00 15.00 15.00 14.00 20.00 15.00
the:DT 20.50 20.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00
Nobel_Prize:NNP 20.50 10.00 15.00 20.00   0.00 12.00 10.00 20.00 10.00
Economics:NNP 20.50   8.08 15.00 20.00 10.00 12.00   8.52 20.00 10.00
The:DT 20.50 20.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00
three:CD   0.00 20.50 19.70 20.50 20.50 18.58 20.50 20.50 20.50
professors:NNS 20.50   3.78 14.34 20.00 10.00 12.00   4.00 20.00 10.00
were:VBD 20.50 15.00 10.00 20.00 15.00 15.00 15.00 20.00 15.00
singled_out:VBN 20.50 15.00 10.00 20.00 15.00 15.00 15.00 20.00 15.00
their:PRP$ 20.50 12.00 15.00 20.00 12.00   0.00 12.00 20.00 12.00
unique:JJ 20.50   8.68   8.39 20.00 12.00 15.00 10.04 20.00 12.00
contributions:NNS 20.50   6.84 15.00 20.00 10.00 12.00   5.63 20.00 10.00
game_theory:NN 20.50 10.00 15.00 20.00 10.00 12.00 10.00 20.00   0.00
derived:VBN 20.50 13.24   6.62 20.00 15.00 15.00 13.25 20.00 15.00
studies:NNS 20.50   4.77 14.42 20.00 10.00 12.00   0.00 20.00 10.00
games:NNS 20.50   8.10 12.99 20.00 10.00 12.00   8.54 20.00 10.00
poker:NN 20.50   8.69 13.30 20.00 10.00 12.00   9.37 20.00 10.00
chess:NN 20.50   8.12 12.29 20.00 10.00 12.00   8.00 20.00 10.00
in:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
which:WDT 20.50 20.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00
players:NNS 20.50   5.52 13.96 20.00 10.00 12.00   7.60 20.00 10.00
have:VBP 20.50 15.00 10.00 20.00 15.00 15.00 15.00 20.00 15.00
to:TO 20.50 20.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00
think:VB 20.50 13.96 10.00 20.00 15.00 15.00 14.09 20.00 15.00
ahead:RB 20.50 15.00 20.00 20.00 15.00 15.00 15.00 20.00 15.00
devise:VB 20.50 14.94   9.94 20.00 15.00 15.00 14.56 20.00 15.00
a:DT 20.50 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00
strategy:NN 20.50   7.47 14.54 20.00 10.00 12.00   8.00 20.00 10.00
expected:VBN 20.50 14.92   9.27 20.00 15.00 15.00 15.00 20.00 15.00
countermoves:NNS 20.50   9.31 15.00 20.00 10.00 12.00   8.04 20.00 10.00
opponents:NNS 20.50   7.01 13.70 20.00 10.00 12.00   8.59 20.00 10.00
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.07 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.44 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "have" of "game_theory" dropped on aligned hyp word "game_theory"
-0.10  1.00 NullPunisher.article : a
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.functionWord : their
 1.00  1.00 Quant.contract : [the,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "shared" aligned badly to "awarded"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "professors" <-nsubjpass-- "singled_out" vs. hyp "scholars" <-nsubj-- "shared", which aligned to text "awarded" args have different parents, different relations: text "studies" <-prep_from-- "derived" vs. hyp "studies" <-prep_for-- "shared", which aligned to text "awarded" args have different parents, different relations: text "game_theory" <-prep_to-- "singled_out" vs. hyp "game_theory" <-prep_about-- "shared", which aligned to text "awarded" args have different parents, different relations: text "game_theory" <-nsubjpass-- "derived" vs. hyp "game_theory" <-prep_about-- "shared", which aligned to text "awarded"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.2931
Threshold: -1.8794


Inference ID: 112

Txt: NIH's FY05 budget request of $28.8 billion includes $2 billion for the National Institute of General Medical Sciences, a 3.4-percent increase, and $1.1 billion for the National Center for Research Resources, and a 7.2-percent decrease from FY04 levels.

Hyp: A request was reduced to $1.1 billion (don't know)

A
DT
request
NN
was
VBD
reduced
VBN
$
$
1.1
CD
billion
CD
NIH:NNP 20.50   9.34 15.50 15.50 20.50 20.50 20.50
FY05:NN 20.00 10.00 15.00 15.00 20.50 20.50 20.50
budget:NN 20.00   6.73 15.00 12.90 18.41 18.84 18.08
request:NN 20.00   0.00 15.00 14.83 19.80 20.24 19.99
$:$ 10.50 19.80 20.50 20.02   0.00 15.75 16.94
28.8:CD 20.50 18.91 20.50 20.50 18.21   5.00   5.00
billion:CD 20.50 19.99 20.50 20.50 16.94   5.00   0.00
includes:VBZ 20.00 14.21 10.00   8.41 18.08 19.74 19.92
$:$ 10.50 19.80 20.50 20.02   0.00 15.75 16.94
2:CD 20.50 19.94 20.50 19.97 18.19   5.00   5.00
billion:CD 20.50 19.99 20.50 20.50 16.94   5.00   0.00
the:DT 10.00 20.00 20.00 20.00 10.50 20.50 20.50
National_Institute_of_General_Medical_Sciences:NNP 20.50   8.14 15.50 15.50 20.50 20.50 20.50
a:DT   0.00 20.00 20.00 20.00 10.50 20.50 20.50
3.4-percent:JJ 20.00 12.00 12.00 11.06 19.29 16.87 18.13
increase:NN 20.00   6.58 15.00   8.85 20.50 19.58 18.84
$:$ 10.50 19.80 20.50 20.02   0.00 15.75 16.94
1.1:CD 20.50 20.24 20.50 20.50 15.75   0.00   0.00
billion:CD 20.50 19.99 20.50 20.50 16.94   0.00   0.00
the:DT 10.00 20.00 20.00 20.00 10.50 20.50 20.50
National_Center_for_Research_Resources:NNP 20.50   8.14 15.50 15.50 20.50 20.50 20.50
a:DT   0.00 20.00 20.00 20.00 10.50 20.50 20.50
7.2-percent:JJ 20.00 12.00 12.00 11.49 20.00 18.51 19.11
decrease:NN 20.00   7.57 15.00 10.17 20.50 17.69 19.52
FY04:CD 20.50 20.50 20.50 20.50 20.50 10.50 10.50
levels:NNS 20.00   6.47 15.00 11.07 20.50 20.50 20.15
NO_WORD   1.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.94 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.14 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added $[$-$]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "$" of "request" dropped on aligned hyp word "request"
-3.00  1.00 NullPunisher.entity : $
-1.00  1.00 NullPunisher.other : reduced
-0.10  1.00 NullPunisher.article : A
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 RootEntailment.unalignedRoot : "reduced" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.5180
Threshold: -1.8794


Inference ID: 2137

Txt: Swedish Foreign Minister murdered.

Hyp: Swedish prime minister murdered. (don't know)

Swedish
JJ
prime_minister
NN
murdered
VBN
Swedish:NNP   0.00   9.53 15.00
Foreign_Minister:NNP 12.00 10.00 15.00
murdered:VBN 12.00 15.00   0.00
NO_WORD   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.74 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
Hand-tuned score (dot product of above): 1.9890
Threshold: -1.8794


Inference ID: 1160

Txt: Baghdad had announced that it will stop cooperating with UNSCOM completely but indicated that it will not ask for their departure.

Hyp: Baghdad will end it's cooperation with UNSCOM, and wants them to leave. (don't know)

Baghdad
NNP
will
MD
end
VB
it
PRP
cooperation
NN
UNSCOM
NNP
wants
VBZ
them
PRP
to
TO
leave
VB
Baghdad:NNP   0.00 20.50 15.50 12.50 10.01   9.44 15.50 12.50 20.50 15.50
had:VBD 15.50 20.00 10.00 15.00 15.00 15.50 10.00 15.00 20.00 10.00
announced:VBN 15.50 20.00   8.36 15.00 14.16 15.50   8.31 15.00 20.00   6.92
that:IN 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.00
it:PRP 12.50 20.00 15.00   0.00 12.00 12.50 15.00   7.32 20.00 15.00
will:MD 20.50   0.00 20.00 20.00 20.00 20.50 20.00 20.00 10.00 20.00
stop:VB 15.50 20.00   7.44 15.00 15.00 15.50   6.68 15.00 20.00   6.36
cooperating:VBG 15.50 20.00   9.92 15.00   1.00 15.50   8.41 15.00 20.00   9.39
UNSCOM:NNP   9.44 20.50 15.50 12.50 10.50   0.00 15.50 12.50 20.50 15.50
completely:RB 15.50 20.00 19.66 15.00 13.36 15.50 18.65 15.00 20.00 19.82
indicated:VBD 15.50 20.00 10.00 15.00 13.50 15.50 10.00 15.00 20.00   7.37
that:IN 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.00
it:PRP 12.50 20.00 15.00   0.00 12.00 12.50 15.00   7.32 20.00 15.00
will:MD 20.50   0.00 20.00 20.00 20.00 20.50 20.00 20.00 10.00 20.00
not:RB 15.50 20.00 20.00 15.00 15.00 15.50 20.00 15.00 20.00 20.00
ask:VB 15.50 20.00 10.00 15.00 15.00 15.50   7.59 15.00 20.00   5.99
their:PRP$ 12.50 20.00 15.00 10.00 12.00 12.50 15.00   0.00 20.00 15.00
departure:NN 10.17 20.00 13.52 12.00   6.59 10.50 15.00 12.00 20.00 12.35
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.00 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  7.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "completely" of "cooperating" dropped on aligned hyp word "cooperation"
-1.00  1.00 NullPunisher.other : end
-1.00  1.00 NullPunisher.other : them
-1.00  1.00 NullPunisher.other : leave
-1.00  1.00 NullPunisher.other : it
-0.05  1.00 NullPunisher.aux : will
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : wants
-2.00  1.00 RootEntailment.unalignedRoot : "end" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.7509
Threshold: -1.8794


Inference ID: 1152

Txt: An Iraqi official reported today, Saturday, that 68 Iraqi civilians were killed as a result of the American and British bombing on Iraq and that their funerals were held today in Baghdad.

Hyp: The 68 civilians killed as a result of the Anglo-American bombing of Iraq, were buried today in Baghdad. (yes)

The
DT
68
CD
civilians
NNS
killed
VBN
a
DT
result
NN
the
DT
Anglo-American
NNP
bombing
NN
Iraq
NNP
were
VBD
buried
VBN
today
NN
Baghdad
NNP
An:DT 10.00 20.50 20.00 20.00 10.00 20.00 10.00 20.00 20.00 20.50 20.00 20.00 20.00 20.50
Iraqi:NNP 20.50 20.50 10.50 15.50 20.50 10.50 20.50 10.50 10.50   3.00 15.50 15.50 10.50   9.04
official:NN 20.00 20.50   7.98 14.49 20.00   6.38 20.00   8.22   7.96   8.95 15.00 15.00   7.43   9.63
reported:VBD 20.00 16.71 14.08   8.59 20.00 12.66 20.00 15.00 14.20 15.50 10.00   8.63 13.44 15.50
today:NN 20.00 19.44   9.31 15.00 20.00   7.33 20.00   9.43   9.30   9.77 15.00 15.00   0.00 10.18
Saturday:NNP 20.50 20.50 10.11 15.50 20.50   8.55 20.50 10.19 10.10 10.08 15.50 15.50   7.86 10.36
that:IN 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.50
68:CD 20.50   0.00 20.30 20.50 20.50 19.98 20.50 20.50 19.53 20.50 20.50 18.73 19.44 20.50
Iraqi:JJ 20.00 20.50 12.00 12.00 20.00 12.00 20.00 12.00 12.00   5.50 12.00 12.00 12.00 12.50
civilians:NNS 20.00 20.30   0.00   8.95 20.00   8.34 20.00   8.72   4.64   9.91 15.00 10.95   9.31   9.98
were:VBD 20.00 20.50 15.00 10.00 20.00 15.00 20.00 15.00 15.00 15.50   0.00 10.00 15.00 15.50
killed:VBN 20.00 20.50   8.95   0.00 20.00 14.73 20.00 15.00   9.77 15.50 10.00   4.24 15.00 15.50
a:DT 10.00 20.50 20.00 20.00   0.00 20.00 10.00 20.00 20.00 20.50 20.00 20.00 20.00 20.50
result:NN 20.00 19.98   8.34 14.73 20.00   0.00 20.00   8.97   8.78   9.24 15.00 14.29   7.33   9.84
the:DT   0.00 20.50 20.00 20.00 10.00 20.00   0.00 20.00 20.00 20.50 20.00 20.00 20.00 20.50
American:JJ 20.00 20.50 12.00 12.00 20.00 12.00 20.00   7.00 12.00 12.50 12.00 12.00 12.00 12.50
British:JJ 20.00 20.50 12.00 12.00 20.00 12.00 20.00 12.00 12.00 12.50 12.00 12.00 12.00 12.50
bombing:NN 20.00 19.53   4.64   9.77 20.00   8.78 20.00   9.90   0.00 10.34 15.00 11.43   9.30 10.47
Iraq:NNP 20.50 20.50   9.91 15.50 20.50   9.24 20.50   9.93 10.34   0.00 15.50 15.50   9.77   6.98
that:IN 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.50
their:PRP$ 20.00 20.50 12.00 15.00 20.00 12.00 20.00 12.00 12.00 12.50 15.00 15.00 12.00 12.50
funerals:NNS 20.00 20.50   7.30 10.48 20.00   8.80 20.00   9.90   7.74 10.34 15.00 11.79   9.31 10.48
were:VBD 20.00 20.50 15.00 10.00 20.00 15.00 20.00 15.00 15.00 15.50   0.00 10.00 15.00 15.50
held:VBN 20.00 20.05 14.35   8.42 20.00 14.65 20.00 15.00 14.50 15.50 10.00   8.76 12.70 15.50
today:NN 20.00 19.44   9.31 15.00 20.00   7.33 20.00   9.43   9.30   9.77 15.00 15.00   0.00 10.18
Baghdad:NNP 20.50 20.50   9.98 15.50 20.50   9.84 20.50   9.99 10.47   6.98 15.50 15.50 10.18   0.00
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.57 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  13.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Iraqi" of "civilians" dropped on aligned hyp word "civilians"
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: american -> Anglo-American
-0.10  1.00 NullPunisher.article : The
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "buried" aligned badly to "held"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "civilians" <-nsubjpass-- "killed" vs. hyp "civilians" <-nsubjpass-- "buried", which aligned to text "held"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.3509
Threshold: -1.8794


Inference ID: 1228

Txt: An official of Abyan police, where 16 Western tourists are being held since yesterday, announced that the hostages are held by the Yemeni "Islamic Jihad" group, which is demanding the release of its leader and lifting the embargo on Iraq.

Hyp: The Yemen branch of the "Islamic Jihad" group, kidnapped the 16 Western tourists. (yes)

The
DT
Yemen
NNP
branch
NN
the
DT
Islamic_Jihad
NN
group
NN
kidnapped
VBD
the
DT
16
CD
Western
JJ
tourists
NNS
An:DT 10.00 20.50 20.00 10.00 20.00 20.00 20.00 10.00 20.50 20.00 20.00
official:NN 20.00 10.50   7.50 20.00 10.00   4.72 13.65 20.00 20.50 12.00   7.04
Abyan:NNP 20.50 10.00 10.50 20.50 10.50 10.50 15.50 20.50 20.50 12.50 10.50
police:NNS 20.00 10.50   4.95 20.00 10.00   5.81 10.31 20.00 20.50 12.00   7.34
where:WRB 10.00 20.50 20.00 10.00 20.00 20.00 20.00 10.00 20.50 20.00 20.00
16:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50   0.00 20.50 20.50
Western:JJ 20.00 12.50 12.00 20.00 12.00 12.00 12.00 20.00 20.50   0.00 12.00
tourists:NNS 20.00 10.50   9.06 20.00 10.00   6.92 14.08 20.00 20.50 12.00   0.00
are:VBP 20.00 15.50 15.00 20.00 15.00 15.00 10.00 20.00 20.50 12.00 15.00
being:VBG 20.00 15.50 15.00 20.00 15.00 15.00 10.00 20.00 20.50 12.00 15.00
held:VBN 20.00 15.50 14.79 20.00 15.00 12.07   9.23 20.00 19.44 12.00 15.00
yesterday:NN 20.00 10.50   8.30 20.00 10.00   5.50 15.00 20.00 20.11   7.00   8.58
announced:VBD 20.00 15.50 14.93 20.00 15.00 15.00   8.76 20.00 20.50 12.00 15.00
that:IN 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00
the:DT   0.00 20.50 20.00   0.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00
hostages:NNS 20.00 10.50   9.38 20.00 10.00   7.62 11.30 20.00 20.50 12.00   8.47
are:VBP 20.00 15.50 15.00 20.00 15.00 15.00 10.00 20.00 20.50 12.00 15.00
held:VBN 20.00 15.50 14.79 20.00 15.00 12.07   9.23 20.00 19.44 12.00 15.00
the:DT   0.00 20.50 20.00   0.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00
Yemeni:NNP 20.50   3.00 10.50 20.50 10.50 10.50 15.50 20.50 20.50 12.50 10.50
Islamic_Jihad:NN 20.00 10.50 10.00 20.00   0.00 10.00 15.00 20.00 20.50 12.00 10.00
group:NN 20.00 10.50   6.50 20.00 10.00   0.00 14.76 20.00 20.50 12.00   6.92
which:WDT 10.00 20.50 20.00 10.00 20.00 20.00 20.00 10.00 20.50 20.00 20.00
is:VBZ 20.00 15.50 15.00 20.00 15.00 15.00 10.00 20.00 20.50 12.00 15.00
demanding:VBG 20.00 15.50 14.99 20.00 15.00 14.05   7.61 20.00 19.09 12.00 15.00
the:DT   0.00 20.50 20.00   0.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00
release:NN 20.00 10.50   9.01 20.00 10.00   6.84 14.58 20.00 20.50 12.00   8.83
its:PRP$ 20.00 12.50 12.00 20.00 12.00 12.00 15.00 20.00 20.50 15.00 12.00
leader:NN 20.00 10.50   7.36 20.00 10.00   3.88 14.36 20.00 20.50 12.00   6.44
lifting:VBG 20.00 15.50 15.00 20.00 15.00 15.00 10.00 20.00 20.50 12.00 15.00
the:DT   0.00 20.50 20.00   0.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00
embargo:NN 20.00 10.50   9.40 20.00 10.00   7.67 14.92 20.00 20.50 12.00   9.09
Iraq:NNP 20.50   8.38   9.97 20.50 10.50   8.31 15.50 20.50 20.50 12.50   9.84
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.26 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.36 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "demanding" of "group" dropped on aligned hyp word "group"
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : kidnapped
-2.00  1.00 RootEntailment.unalignedRoot : "kidnapped" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.4960
Threshold: -1.8794


Inference ID: 1197

Txt: Contact with the press will be restricted to "perodic meetings", as promised by all three parties, in order to "focus their energies" on the most important issues.

Hyp: The delegations may only speak periodically to the press, in order to concentrate on the issues. (yes)

The
DT
delegations
NNS
may
MD
only
RB
speak
VB
periodically
RB
the
DT
press
NN
in_order
IN
to
TO
concentrate
VB
the
DT
issues
NNS
Contact:NN 20.00   8.75 20.00 15.00 15.00 15.00 20.00   9.03 20.00 20.00 15.00 20.00   8.49
the:DT   0.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00 20.00 10.00 20.00   0.00 20.00
press:NN 20.00   7.57 20.00 15.00 11.85 14.82 20.00   0.00 20.00 20.00 15.00 20.00   8.28
will:MD 10.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 10.00 20.00
be:VB 20.00 15.00 20.00 20.00 10.00 20.00 20.00 15.00 20.00 20.00 10.00 20.00 15.00
restricted:VBN 20.00 15.00 20.00 20.00 10.00 15.48 20.00 15.00 20.00 20.00 10.00 20.00 13.35
perodic:JJ 20.00 12.00 20.00 12.00 12.00 12.00 20.00 12.00 20.00 20.00 12.00 20.00 12.00
meetings:NNS 20.00   6.44 20.00 15.00 12.05 13.17 18.42   6.24 20.00 18.59 15.00 18.42   7.54
as:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 18.72
promised:VBD 20.00 15.00 20.00 20.00   9.73 20.00 20.00 13.39 20.00 20.00   8.64 20.00 15.00
all:DT 10.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 10.00 20.00
three:CD 20.50 20.50 20.50 20.50 20.50 19.28 20.50 20.50 20.50 20.50 20.28 20.50 20.50
parties:NNS 20.00   6.20 20.00 15.00 13.32 15.00 20.00   7.36 20.00 20.00 14.72 20.00   7.23
in_order:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
to:TO 10.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.00   0.00 20.00 10.00 20.00
focus:VB 20.00 15.00 20.00 20.00   8.47 17.65 20.00 14.65 20.00 20.00   2.80 20.00 12.56
their:PRP$ 20.00 12.00 20.00 20.00 15.00 20.00 20.00 12.00 20.00 20.00 15.00 20.00 12.00
energies:NNS 20.00   7.73 20.00 15.00 14.29 12.92 20.00   8.13 20.00 20.00 11.99 20.00   7.37
the:DT   0.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00 20.00 10.00 20.00   0.00 20.00
most:RBS 20.00 15.00 20.00 10.00 20.00 10.00 20.00 15.00 20.00 20.00 20.00 20.00 15.00
important:JJ 20.00 11.12 20.00 12.00 10.22 11.12 20.00 12.00 20.00 20.00   8.13 20.00   8.95
issues:NNS 20.00   7.90 20.00 15.00 12.90 14.99 20.00   8.28 20.00 20.00 14.58 20.00   0.00
NO_WORD   1.00 10.00 10.00   9.00 10.00   9.00   1.00 10.00 10.00 10.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.75 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  8.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.15 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added periodically[periodically-RB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "important" of "issues" dropped on aligned hyp word "issues"
 2.00  1.00 Modal.yes : actual -> possible
-1.00  1.00 NullPunisher.other : periodically
-1.00  1.00 NullPunisher.other : delegations
-1.00  1.00 NullPunisher.other : speak
-1.00  1.00 NullPunisher.other : in_order
-1.00  1.00 NullPunisher.other : only
-0.10  1.00 NullPunisher.article : The
-0.10  1.00 NullPunisher.functionWord : to
-0.05  1.00 NullPunisher.aux : may
-2.00  1.00 RootEntailment.unalignedRoot : "speak" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.1395
Threshold: -1.8794


Inference ID: 1153

Txt: Sultan Al-Shawi, a.k.a the Attorney, said during a funeral held for the victims, "They were all children of Iraq killed during the savage bombing."

Hyp: The Attorney, said at the funeral, "They were all Iraqis killed during the brutal shelling." (yes)

The
DT
Attorney
NNP
said
VBD
the
DT
funeral
NN
They
PRP
were
VBD
all
DT
Iraqis
NNPS
killed
VBN
the
DT
brutal
JJ
shelling
NN
Sultan_Al-Shawi:NNP 20.50   8.85 15.50 20.50 10.40 12.50 15.50 20.50 10.50 15.50 20.50 12.50 10.27
a.k.a:VBG 20.00 15.00 10.00 20.00 15.00 15.00 10.00 20.00 15.00 10.00 20.00 12.00 15.00
the:DT   0.00 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00 20.00   0.00 20.00 20.00
Attorney:NNP 20.00   0.00 15.00 20.00   9.18 12.00 15.00 20.00 10.00 15.00 20.00 12.00   8.75
said:VBD 20.00 15.00   0.00 20.00 13.88 15.00 10.00 20.00 15.00   8.97 20.00 12.00 14.51
a:DT 10.00 20.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00
funeral:NN 20.00   9.18 13.88 20.00   0.00 12.00 15.00 20.00 10.00 12.33 20.00 11.05   8.62
held:VBN 20.00 15.00   9.49 20.00 14.26 15.00 10.00 20.00 15.00   8.42 20.00 11.65 14.24
the:DT   0.00 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00 20.00   0.00 20.00 20.00
victims:NNS 20.00   6.70 14.68 20.00   8.34 12.00 15.00 20.00 10.00   9.29 20.00   8.84   7.05
victims:NNS 20.00   6.70 14.68 20.00   8.34 12.00 15.00 20.00 10.00   9.29 20.00   8.84   7.05
were:VBD 20.00 15.00 10.00 20.00 15.00 15.00   0.00 20.00 15.00 10.00 20.00 12.00 15.00
all:DT 10.00 20.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00 20.00 10.00 20.00 20.00
children:NNS 20.00   6.94 15.00 20.00   7.61 12.00 15.00 20.00 10.00 13.17 20.00 11.22   9.03
Iraq:NNP 20.50   9.33 15.50 20.50 10.34 12.50 15.50 20.50   5.50 15.50 20.50 12.50 10.17
killed:VBN 20.00 15.00   8.97 20.00 12.33 15.00 10.00 20.00 15.00   0.00 20.00   6.73 10.50
the:DT   0.00 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00 20.00   0.00 20.00 20.00
savage:JJ 20.00 12.00 10.86 20.00 11.56 15.00 12.00 20.00 12.00   9.16 20.00   5.33   9.99
bombing:NN 20.00   9.16 14.94 20.00   8.76 12.00 15.00 20.00 10.00   9.77 20.00   8.37   4.01
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00 10.00   1.00   9.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.02 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.38 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "held" of "funeral" dropped on aligned hyp word "funeral"
-1.00  1.00 NullPunisher.other : all
-0.05  1.00 NullPunisher.aux : were
-1.00  1.00 NullPunisher.other : They
-0.10  1.00 NullPunisher.article : the
 1.00  1.00 Quant.contract : [a,the]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "Iraqis" aligned badly to "Iraq"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Attorney" <-dobj-- "a.k.a" vs. hyp "Attorney" <-nsubj-- "Iraqis", which aligned to text "Iraq"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -5.1116
Threshold: -1.8794


Inference ID: 2095

Txt: Scientists had thought all gamma-ray bursts had a standard energy.

Hyp: The scientists had thought that all explosions gamma the ray have a standard energy. (don't know)

The
DT
scientists
NNS
had
VBD
thought
VBN
that
IN
all
DT
explosions
NNS
gamma
VBD
the
DT
ray
NN
have
VBP
a
DT
standard
JJ
energy
NN
Scientists:NNS 20.00   0.00 15.00 15.00 20.00 20.00   7.54 15.00 20.00   8.29 15.00 20.00 12.00   6.76
had:VBD 20.00 15.00   0.00 10.00 20.00 20.00 15.00 10.00 20.00 15.00   0.00 20.00 12.00 15.00
thought:VBN 20.00 13.94 10.00   0.00 20.00 20.00 15.00 10.00 20.00 12.72 10.00 20.00 11.85 14.08
all:DT 10.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00
gamma-ray:JJ 20.00 11.82 12.00 11.02 20.00 20.00 10.06   6.46 20.00   2.00 12.00 20.00 10.00 11.45
bursts:NNS 20.00   8.05 15.00 15.00 20.00 20.00   5.58 12.71 20.00   9.37 15.00 20.00 10.28   8.46
had:VBD 20.00 15.00   0.00 10.00 20.00 20.00 15.00 10.00 20.00 15.00   0.00 20.00 12.00 15.00
a:DT 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00   0.00 20.00 20.00
standard:JJ 20.00 11.74 12.00 11.85 20.00 20.00 11.68 12.00 20.00 12.00 12.00 20.00   0.00 11.80
energy:NN 20.00   6.76 15.00 14.08 20.00 20.00   7.58 14.73 20.00   2.65 15.00 20.00 11.80   0.00
NO_WORD   1.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.99 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.64 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : all
-0.10  1.00 NullPunisher.functionWord : that
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : The
-1.00  1.00 NullPunisher.other : explosions
 1.00  1.00 Quant.contract : [all,the]
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "gamma-ray" <-nsubj-- "had" vs. hyp "gamma" <-ccomp-- "thought", which aligned to text "thought"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -3.3682
Threshold: -1.8794


Inference ID: 2107

Txt: U.S. crude settled $1.32 lower at $42.83 a barrel.

Hyp: Crude the light American lowered to the closing 1.32 dollars, to 42.83 dollars the barrel. (don't know)

Crude
NN
the
DT
light
JJ
American
NNP
lowered
VBD
the
DT
closing
JJ
1.32
CD
dollars
NNS
42.83
CD
dollars
NNS
the
DT
barrel
NN
U.S.:NNP   8.53 20.50 12.50   7.72 15.50 20.50 12.50 20.50   7.94 20.50   7.94 20.50   9.02
crude:NN   0.00 20.00 10.80   7.80 15.00 20.00 11.29 19.78   8.91 20.50   8.91 20.00   1.21
settled:VBD 15.00 20.00 12.00 15.00   9.82 20.00 11.39 20.50 13.84 20.50 13.84 20.00   9.46
$:$ 20.50 10.50 20.50 20.50 20.50 10.50 20.10 16.74 10.00 20.00 10.00 10.50 18.57
1.32:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.02   0.00 20.00   5.00 20.00 20.50 18.39
lower:RBR 15.00 20.00 10.95 15.00 10.00 20.00 11.55 20.15 14.10 20.50 14.10 20.00 14.34
$:$ 20.50 10.50 20.50 20.50 20.50 10.50 20.10 16.74 10.00 20.00 10.00 10.50 18.57
42.83:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50   5.00 20.00   0.00 20.00 20.50 20.50
a:DT 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.50 20.50 20.50 20.50 10.00 20.00
barrel:NN   8.91 20.00 12.00   8.21 15.00 20.00 11.42 18.39   9.34 20.50   9.34 20.00   0.00
NO_WORD 10.00   1.00   9.00 10.00 10.00   1.00   9.00 10.00 10.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.77 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  8.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.15 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added dollars[dollars-NNS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "U.S." of "crude" dropped on aligned hyp word "Crude"
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-3.00  1.00 NullPunisher.entity : dollars
-1.00  1.00 NullPunisher.other : American
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : closing
-3.00  1.00 NullPunisher.entity : dollars
-1.00  1.00 NullPunisher.other : light
 1.00  1.00 Quant.contract : [a,the]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "lowered" aligned badly to "lower"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "crude" <-nsubj-- "settled" vs. hyp "Crude" <-nsubj-- "lowered", which aligned to text "lower"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -15.3593
Threshold: -1.8794


Inference ID: 1190

Txt: The Commission, designated to shed light on crimes and human rights violations during the apartheid period, accuses the African National Congress of carrying out torture and executions without trials in concentration camps, and also of playing an active role in the political violence incidents that took place between 1990 and 1994.

Hyp: The Commission accused the ANC of violence, executions and human rights violations. (yes)

The
DT
Commission
NNP
accused
VBD
the
DT
ANC
NNP
violence
NN
executions
NNS
human
JJ
rights
NNS
violations
NNS
The:DT   0.00 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00
Commission:NNP 20.50   0.00 15.50 20.50 10.00   8.29   8.90 12.50   7.65   8.73
designated:VBN 20.00 15.50 10.00 20.00 15.50 15.00 14.60 11.42 13.33 12.10
to:TO 10.00 20.50 20.00 10.00 20.50 20.00 20.00 20.00 20.00 20.00
shed:VB 20.00 15.50   9.91 20.00 15.50 15.00 15.00 12.00 15.00 15.00
light:JJ 20.00 12.50 12.00 20.00 12.50 11.46 11.34   9.35   7.00 10.23
crimes:NNS 20.00   7.67 11.12 20.00 10.50   4.20   5.14 10.40   7.21   2.04
human:JJ 20.00 12.50 12.00 20.00 12.50   8.72   8.78   0.00   7.85   9.24
rights:NNS 20.00   7.65 12.49 20.00 10.50   6.02   6.65   7.85   0.00   6.87
violations:NNS 20.00   8.73 11.44 20.00 10.50   7.93   6.99   9.24   6.87   0.00
the:DT   0.00 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00
apartheid:NN 20.00   9.13 14.08 20.00 10.50   7.59   8.12 10.79   7.16   9.10
period:NN 20.00   6.38 15.00 20.00 10.50   6.73   7.51 10.71   5.93   7.29
accuses:VBZ 20.00 15.50   0.00 20.00 15.50 15.00 14.66 12.00 11.05 11.91
the:DT   0.00 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00
African_National_Congress:NNP 20.50   8.45 15.50 20.50 10.00   9.41   9.79 12.50   8.98   9.69
of:IN 20.00 20.50 20.00 18.70 20.50 20.00 20.00 20.00 20.00 20.00
carrying_out:VBG 20.00 15.50 10.00 20.00 15.50 15.00 15.00 12.00 15.00 15.00
torture:NNS 20.00   9.67 11.62 20.00 10.50   6.26   4.49   7.69   7.09   6.13
executions:NNS 20.00   8.90 13.71 20.00 10.50   7.37   0.00   8.78   6.65   6.99
trials:NNS 20.00   8.59 12.90 20.00 10.50   7.77   6.97   9.02   7.14   8.00
concentration_camps:NNS 20.00   9.99 15.00 20.00 10.50   9.71   9.87 12.00   9.50   9.83
also:RB 20.00 15.50 20.00 20.00 15.50 15.00 15.00 12.00 15.00 15.00
of:IN 20.00 20.50 20.00 18.70 20.50 20.00 20.00 20.00 20.00 20.00
playing:VBG 20.00 15.50   9.99 20.00 15.50 14.58 15.00 11.92 14.97 14.08
an:DT 10.00 20.50 20.00 10.00 20.50 20.00 20.00 20.00 20.00 20.00
active:JJ 20.00 12.50 10.78 20.00 12.50 10.12 10.84   7.49   8.59 10.28
role:NN 20.00   7.48 12.03 20.00 10.50   5.77   7.37   9.24   7.02   6.60
the:DT   0.00 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00
political:JJ 20.00 12.50 10.50 20.00 12.50   9.68 10.87   8.34   9.51 12.00
violence:NN 20.00   8.29 12.34 20.00 10.50   0.00   7.37   8.72   6.02   7.93
incidents:NNS 20.00   8.70 11.73 20.00 10.50   4.65   6.37 10.66   8.23   5.32
that:WDT 10.00 20.50 20.00 10.00 20.50 20.00 20.00 20.00 20.00 20.00
took:VBD 20.00 15.50   6.86 20.00 15.50 15.00 15.00 12.00 15.00 14.22
place:NN 20.00   7.65 15.00 20.00 10.50   7.83   8.43 10.94   7.20   8.27
1990:CD 20.50 20.50 17.42 20.50 20.50 20.50 19.70 20.50 19.04 18.25
1994:CD 20.50 20.50 18.95 20.50 20.50 20.50 20.05 20.50 19.97 18.78
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00 10.00   9.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.42 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  10.00 Alignment.hypSpan
 0.10  0.30 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "light" of "violations" dropped on aligned hyp word "violations"
-1.00  1.00 Structure.relMismatch : noun args have different parents but same relations: "human": "rights" vs. "violations"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.3665
Threshold: -1.8794


Inference ID: 1159

Txt: The Commission's Deputy Chairman, Charles Duelfer, said, "We will keep the inspectors there in the hope that Iraq may back down from its decision."

Hyp: The Vice-Chairman of the Committee Charles Duelfer said, "we hope to end Iraq's resolution." (don't know)

The
DT
Vice-Chairman
NN
the
DT
Committee
NNP
Charles_Duelfer
NNP
said
VBD
we
PRP
hope
VBP
to
TO
end
VB
Iraq
NNP
resolution
NN
The:DT   0.00 20.00   0.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.00
Commission:NNP 20.50   7.92 20.50   0.50   8.71 15.50 12.50 15.50 20.50 15.50   9.47   8.74
Deputy:NNP 20.00   8.58 20.00   8.36   8.76 15.00 12.00 15.00 20.00 15.00   9.84   9.14
Chairman:NNP 20.00   5.00 20.00   6.97   5.38 15.00 12.00 15.00 20.00 15.00   9.05   8.15
Charles_Duelfer:NNP 20.50   8.94 20.50   8.71   0.00 15.50 12.50 15.50 20.50 15.50   9.80   9.54
said:VBD 20.00 15.00 20.00 15.00 15.50   0.00 15.00   8.48 20.00 10.00 15.50 15.00
We:PRP 20.00 12.00 20.00 12.00 12.50 15.00   0.00 15.00 20.00 15.00 12.50 12.00
will:MD 10.00 20.00 10.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.00
keep:VB 20.00 15.00 20.00 15.00 15.50   9.25 15.00   5.45 20.00   8.17 15.50 14.17
the:DT   0.00 20.00   0.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.00
inspectors:NNS 20.00   8.89 20.00   8.70   8.87 14.06 12.00 15.00 20.00 15.00   9.88   9.37
there:RB 20.00 15.00 20.00 15.00 15.50 20.00 13.53 20.00 20.00 20.00 15.50 15.00
the:DT   0.00 20.00   0.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.00
hope:NN 20.00   8.43 20.00   8.20   9.50 13.48 12.00   0.00 20.00 13.07 10.01   7.46
that:IN 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.50 20.00
Iraq:NNP 20.50   9.64 20.50   9.47   9.80 15.50 12.50 15.50 20.50 15.50   0.00 10.04
may:MD 10.00 20.00 10.00 20.00 20.50 20.00 20.00 20.00 10.00 20.00 20.50 20.00
back_down:VB 20.00 15.00 20.00 15.00 15.50 10.00 15.00 10.00 20.00 10.00 15.50 15.00
its:PRP$ 20.00 12.00 20.00 12.00 12.50 15.00 10.00 15.00 20.00 15.00 12.50 12.00
decision:NN 20.00   8.02 20.00   7.75   9.18 13.48 12.00 12.95 20.00 14.09   9.80   6.78
NO_WORD   1.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.93 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Committee[Committee-NNP]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Deputy" of "Chairman" dropped on aligned hyp word "Vice-Chairman"
-1.00  1.00 NullPunisher.other : Committee
-1.00  1.00 NullPunisher.other : resolution
-0.10  1.00 NullPunisher.functionWord : to
-0.10  1.00 NullPunisher.article : The
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : end
-2.00  1.00 Person.mismatch : person mimatch between we and We
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "hope" <-prep_in-- "keep" vs. hyp "hope" <-ccomp-- "said", which aligned to text "said"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -8.6679
Threshold: -1.8794


Inference ID: 1186

Txt: The Prime Minister's Office and the Foreign Office had earlier purposely asserted that the case is strictly in the jurisdiction of the police and the justice system.

Hyp: The jurisdiction of the case was queried by the Prime Minister and the Ministry of Foreign Affairs. (don't know)

The
DT
jurisdiction
NN
the
DT
case
NN
was
VBD
queried
VBN
the
DT
Prime_Minister
NNP
the
DT
Ministry_of_Foreign_Affairs
NNP
The:DT   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
Prime_Minister:POS 10.00 20.00 10.00 20.00 20.00 20.00 10.00   0.00 10.00 20.50
Office:NNP 20.50   9.50 20.50   8.20 15.50 15.50 20.50   8.65 20.50   8.45
the:DT   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
Foreign_Office:NNP 20.50 10.50 20.50 10.50 15.50 15.50 20.50 10.50 20.50 10.00
had:VBD 20.00 15.00 20.00 15.00 10.00 10.00 20.00 15.00 20.00 15.50
earlier:RBR 20.00 15.00 20.00 14.02 20.00 20.00 20.00 15.00 20.00 15.50
purposely:RB 20.00 12.87 20.00 13.26 20.00 20.00 20.00 15.00 20.00 15.50
asserted:VBN 20.00 12.84 20.00 11.13 10.00   6.85 20.00 15.00 20.00 15.50
that:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50
the:DT   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
case:NN 20.00   4.61 20.00   0.00 15.00 14.70 20.00   8.54 20.00   8.83
is:VBZ 20.00 15.00 20.00 15.00   0.00 10.00 20.00 15.00 20.00 15.50
strictly:RB 20.00 11.85 20.00 13.37 20.00 20.00 20.00 15.00 20.00 15.50
the:DT   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
jurisdiction:NN 20.00   0.00 20.00   4.61 15.00 15.00 20.00   9.49 20.00   9.87
the:DT   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
police:NN 20.00   9.07 20.00   7.56 15.00 14.45 20.00   8.73 20.00   7.84
the:DT   0.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
justice:NN 20.00   4.62 20.00   3.21 15.00 14.00 20.00   8.62 20.00   8.91
system:NN 20.00   8.43 20.00   7.26 15.00 14.65 20.00   8.33 20.00   7.44
NO_WORD   1.00 10.00   1.00 10.00   1.00 10.00   1.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.11 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "police" of "jurisdiction" dropped on aligned hyp word "jurisdiction"
-3.00  1.00 NullPunisher.entity : Ministry_of_Foreign_Affairs
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : queried
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.unalignedRoot : "queried" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.3270
Threshold: -1.8794


Inference ID: 1156

Txt: Iraq's representative to the United Nations, Nizar Hamdoun, announced today, Sunday, that thousands of people were killed or injured during the four days of air bombardment against Iraq.

Hyp: Nizar HAMDOON, Iraqi ambassador to the United Nations, announced that thousands of people could be killed or wounded due to the aerial bombardment of Iraq. (don't know)

Nizar_HAMDOON_,_Iraqi
NNP
ambassador
NN
the
DT
United_Nations
NNP
announced
VBD
that
IN
thousands
NNS
people
NNS
could
MD
be
VB
killed
VBN
wounded
VBN
the
DT
aerial
JJ
bombardment
NN
Iraq
NNP
Iraq:NNP 10.00   9.87 20.50 10.22 15.50 20.50   9.80   8.97 20.50 15.50 15.50 15.50 20.50 12.50 10.43   0.00
representative:NN 10.50   3.66 20.00   8.91 14.94 20.00   7.38   5.71 20.00 15.00 15.00 15.00 20.00 12.00   9.11   8.86
the:DT 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
United_Nations:NNP 10.50 10.09 20.50   0.00 15.50 20.50   9.58   7.47 20.50 15.50 15.50 15.50 20.50 12.50 10.36 10.22
Nizar_Hamdoun:NNP 10.50 10.50 20.50 10.50 15.50 20.50 10.50 10.50 20.50 15.50 15.50 15.50 20.50 12.50 10.50 10.50
announced:VBD 15.50 14.85 20.00 15.50   0.00 20.00 13.62 15.00 20.00 10.00   9.33   9.26 20.00 11.23 15.00 15.50
today:NN 10.50   9.04 20.00   9.54 12.88 20.00   7.57   6.91 20.00 15.00 15.00 15.00 20.00 12.00   9.54   9.77
Sunday:NNP 10.50   9.77 20.50   9.77 15.50 20.50   7.65   7.88 20.50 15.50 15.50 15.50 20.50 12.50 10.18   9.96
that:IN 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50
thousands:NNS 10.50   9.08 20.00   9.58 13.62 20.00   0.00   6.83 20.00 15.00 10.14 11.11 20.00 10.79   7.53   9.80
people:NNS 10.50   8.11 20.00   7.47 15.00 20.00   6.83   0.00 20.00 15.00 11.02 12.15 20.00 11.39   7.61   8.97
were:VBD 15.50 15.00 20.00 15.50 10.00 20.00 15.00 15.00 18.68   0.00 10.00 10.00 20.00 12.00 15.00 15.50
killed:VBN 15.50 14.34 20.00 15.50   9.33 20.00 10.14 11.02 20.00 10.00   0.00   1.16 20.00 10.99 11.74 15.50
injured:VBN 15.50 15.00 20.00 15.50   8.84 20.00 10.09 12.35 20.00 10.00   3.02   3.35 20.00 10.64 12.93 15.50
the:DT 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
four:CD 20.50 20.50 20.50 20.50 20.50 20.50 19.17 20.50 20.50 20.50 19.63 19.52 20.50 20.35 18.91 20.50
days:NNS 10.50   8.62 20.00   9.13 13.71 20.00   5.81   6.10 20.00 15.00 14.48 13.62 20.00 11.56   9.16   9.42
air:NN 10.50   7.68 20.00   9.59 14.71 20.00   8.32   7.01 20.00 15.00 13.26 13.07 20.00   7.11   5.37   9.55
bombardment:NN 10.50   8.64 20.00 10.36 15.00 20.00   7.53   7.61 20.00 15.00 11.74 11.33 20.00   6.61   0.00 10.43
Iraq:NNP 10.00   9.87 20.50 10.22 15.50 20.50   9.80   8.97 20.50 15.50 15.50 15.50 20.50 12.50 10.43   0.00
NO_WORD 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.17 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added aerial[aerial-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "air" of "bombardment" dropped on aligned hyp word "bombardment"
-0.05  1.00 NullPunisher.aux : could
-0.10  1.00 NullPunisher.functionWord : that
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : wounded
-1.00  1.00 NullPunisher.other : aerial
-3.00  1.00 NullPunisher.entity : Nizar_HAMDOON_,_Iraqi
-1.00  1.00 Structure.relMismatch : text "killed" is dep of "announced" while hyp "killed" is ccomp of "announced" which aligned to text "announced"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.3635
Threshold: -1.8794


Inference ID: 1226

Txt: Ecevit, one of the most senior Turkish politicians, was unable to secure the necessary support from the parties represented in the Parliament in order to form his government.

Hyp: Ecevit was unable to form the government due to a lack of support. (yes)

Ecevit
NNP
was
VBD
unable
JJ
to
TO
form
VB
the
DT
government
NN
due
JJ
a
DT
lack
NN
support
NN
Ecevit:NNP   0.50 15.50 12.50 20.50 15.50 20.50 10.50 12.50 20.50 10.50 10.50
one:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
the:DT 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.00 10.00 20.00 20.00
most:RBS 15.00 20.00 12.00 20.00 20.00 20.00 15.00 12.00 20.00 15.00 15.00
senior:JJ 12.00 12.00 10.00 20.00 12.00 20.00 12.00   7.54 20.00 11.16 11.36
Turkish:JJ 12.00 12.00 10.00 20.00 12.00 20.00 12.00 10.00 20.00 12.00 12.00
politicians:NNS 10.00 15.00 12.00 20.00 12.34 20.00   5.53 12.00 20.00   7.54   6.49
was:VBD 15.00   0.00 12.00 20.00 10.00 20.00 15.00 12.00 20.00 15.00 15.00
unable:JJ 12.00 12.00   0.00 20.00   8.77 20.00 10.21   9.08 20.00   8.15   8.92
to:TO 20.00 20.00 20.00   0.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00
secure:VB 15.00 10.00   7.26 20.00   7.96 20.00 14.09 11.31 20.00 11.40 10.60
the:DT 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.00 10.00 20.00 20.00
necessary:JJ 12.00 12.00   6.64 20.00   8.42 20.00   9.34   9.17 20.00   7.81   9.85
support:NN 10.00 15.00   8.92 20.00 13.84 20.00   7.43 12.00 20.00   6.24   0.00
the:DT 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.00 10.00 20.00 20.00
parties:NNS 10.00 15.00 10.75 20.00 10.66 20.00   4.88 11.74 20.00   8.32   5.56
represented:VBN 15.00 10.00 12.00 20.00   9.30 20.00 15.00 11.18 20.00 15.00 14.06
the:DT 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.00 10.00 20.00 20.00
Parliament:NNP 10.50 15.50 12.50 20.50 15.50 20.50   7.26 12.50 20.50   9.57   9.47
in_order:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
to:TO 20.00 20.00 20.00   0.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00
form:VB 15.00 10.00   8.77 20.00   0.00 20.00 11.24 10.91 20.00 14.59 13.84
his:PRP$ 12.00 15.00 15.00 20.00 15.00 20.00 12.00 15.00 20.00 12.00 12.00
government:NN 10.00 15.00 10.21 20.00 11.24 20.00   0.00 11.14 20.00   7.60   7.43
NO_WORD 10.00   1.00   9.00 10.00 10.00   1.00 10.00   9.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.12 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.18 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added lack[lack-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "necessary" of "support" dropped on aligned hyp word "support"
-1.00  1.00 NullPunisher.other : due
-0.10  1.00 NullPunisher.article : a
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : lack
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 Structure.relMismatch : text "form" is advcl of "unable" while hyp "form" is xcomp of "unable" which aligned to text "unable"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.4664
Threshold: -1.8794


Inference ID: 1155

Txt: The American television network "CNN" reported that officials at the American Defense Department (The Pentagon) will recommend halting the bombing on Iraq to President Bill Clinton this evening, Saturday.

Hyp: CNN reported that officials at the Pentagon said that President Clinton is obligated to stop the shelling on Iraq . (don't know)

CNN
NNP
reported
VBD
that
IN
officials
NNS
the
DT
Pentagon
NNP
said
VBD
that
IN
President_Clinton
NNP
is
VBZ
obligated
VBN
to
TO
stop
VB
the
DT
shelling
NN
Iraq
NNP
The:DT 20.50 20.00 20.00 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.50
American:NNP 10.50 15.50 20.50   6.04 20.50   8.30 15.50 20.50   8.58 15.50 15.50 20.50 15.50 20.50   9.18   8.76
television:NN   9.04 13.61 20.00   6.51 20.00   8.00 15.00 20.00   8.65 15.00 15.00 20.00 15.00 20.00   8.83   9.40
network:NN   8.99 14.17 20.00   7.61 20.00   9.27 15.00 20.00   9.24 15.00 15.00 20.00 15.00 20.00   9.04   9.85
CNN:NNP   0.50 15.00 20.00 10.00 20.00   9.78 15.00 20.00 10.00 15.00 15.00 20.00 15.00 20.00 10.00 10.50
reported:VBD 15.50   0.00 20.00 12.57 20.00 15.50   7.90 20.00 15.00 10.00 10.00 20.00 10.00 20.00 14.00 15.50
that:IN 20.50 20.00   0.00 20.00 20.00 20.50 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50
officials:NNS 10.50 12.57 20.00   0.00 20.00   7.86 12.80 20.00   7.68 15.00 13.50 20.00 12.69 20.00   7.74   8.95
the:DT 20.50 20.00 20.00 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.50
American_Defense_Department:NNP 10.00 15.50 20.50   6.04 20.50   7.80 15.50 20.50   8.58 15.50 15.50 20.50 15.50 20.50   9.18   9.26
The:DT 20.50 20.00 20.00 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.50
Pentagon:NNP   9.78 15.00 20.00   7.36 20.00   0.50 15.00 20.00   9.10 15.00 15.00 20.00 15.00 20.00   9.26   9.81
will:MD 20.50 20.00 20.00 20.00 10.00 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00 10.00 20.00 20.50
recommend:VB 15.50   9.72 20.00 12.97 20.00 15.50   8.81 20.00 15.00 10.00   7.95 20.00   9.28 20.00 13.89 15.50
halting:VBG 15.50   9.42 20.00 13.89 20.00 15.50 10.00 20.00 15.00 10.00   8.44 20.00   7.45 20.00 13.49 15.50
the:DT 20.50 20.00 20.00 20.00   0.00 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.00 20.50
bombing:NN 10.50 14.20 20.00   6.37 20.00 10.04 14.94 20.00   9.79 15.00 15.00 20.00 12.63 20.00   4.01 10.34
Iraq:NNP 10.50 15.50 20.50   8.95 20.50   9.81 15.50 20.50   9.89 15.50 15.50 20.50 15.50 20.50 10.17   0.00
President:NNP 10.50 15.00 20.00   5.35 20.00   8.17 15.00 20.00   5.00 15.00 15.00 20.00 15.00 20.00   8.59   9.17
Bill_Clinton:NNP 10.50 15.50 20.50   8.18 20.50   9.60 15.50 20.50   0.50 15.50 15.50 20.50 15.50 20.50 10.09   9.89
this:DT 20.50 20.50 18.54 20.50 10.50 20.50 20.50 18.54 20.50 20.50 20.50 10.50 20.50 10.50 20.50 20.50
evening:NN 10.50 12.57 20.50   8.80 20.50   9.72 15.06 20.50 10.07 15.50 15.50 20.50 15.45 20.50   7.75 10.15
Saturday:NNP 10.50 15.50 20.50   8.63 20.50   9.61 15.50 20.50   9.99 15.50 15.50 20.50 15.50 20.50   9.83 10.08
NO_WORD 10.00 10.00   1.00 10.00   1.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.88 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  0.38 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Bill_Clinton" of "Iraq" dropped on aligned hyp word "Iraq"
-1.00  1.00 NullPunisher.other : obligated
-0.10  1.00 NullPunisher.functionWord : to
-0.10  1.00 NullPunisher.functionWord : that
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "CNN" <-dep-- "network" vs. hyp "CNN" <-nsubj-- "reported", which aligned to text "reported"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.1708
Threshold: -1.8794


Inference ID: 2099

Txt: The cost of the consumer of the United States fell in June.

Hyp: U.S. consumer spending dived in June. (don't know)

U.S.
NNP
consumer
NN
spending
NN
dived
VBD
June
NNP
The:DT 20.50 20.00 20.00 20.00 20.50
cost:NN   6.66   6.26   6.89 15.00   7.71
the:DT 20.50 20.00 20.00 20.00 20.50
consumer:NN   7.52   0.00   7.46 15.00   8.41
the:DT 20.50 20.00 20.00 20.00 20.50
United_States:NNP   0.00   6.72   8.17 15.50   8.21
fell:VBD 15.50 14.43 14.70   7.80 15.50
June:NNP   8.35   8.41   9.04 15.50   0.00
NO_WORD 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.80 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 06/01/1000
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "dived" aligned badly to "fell"
Hand-tuned score (dot product of above): 0.7172
Threshold: -1.8794


Inference ID: 1191

Txt: The Truth and Reconciliation Commission's investigators submitted the preliminary results of their work in early October to around 200 organisations and individuals to warn them of charges against them and to give them the opportunity to respond before final drafting of the report.

Hyp: The Commission investigated 200 people and oragnisations, before charging them, in order to prevent them from responding prior to the official release of the report. (don't know)

The
DT
Commission
NNP
investigated
VBD
200
CD
people
NNS
oragnisations
NNS
before
IN
charging
VBG
them
PRP
in_order
IN
to
TO
prevent
VB
them
PRP
from
IN
responding
VBG
the
DT
official
JJ
release
NN
the
DT
report
NN
The:DT   0.00 20.50 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00
Truth_and_Reconciliation_Commission:NNP 20.50   5.00 15.50 20.50   8.52 10.50 20.50 15.50 12.50 20.50 20.50 15.50 12.50 20.50 15.50 20.50 12.50   9.85 20.50   9.35
investigators:NNS 20.00   8.56   1.00 20.50   7.35 10.00 20.00 13.37 12.00 20.00 20.00 12.66 12.00 20.00 11.54 20.00   8.56   8.47 20.00   7.45
submitted:VBD 20.00 15.50   5.80 19.80 15.00 15.00 20.00   7.73 15.00 20.00 20.00   8.86 15.00 20.00   8.12 20.00   8.46 11.14 20.00 11.89
the:DT   0.00 20.50 20.00 20.50 20.00 20.00 20.00 20.00 15.71 20.00 10.00 20.00 15.71 20.00 20.00   0.00 20.00 20.00   0.00 20.00
preliminary:JJ 20.00 12.50 10.36 20.25 12.00 12.00 20.00 12.00 15.00 20.00 20.00 11.51 15.00 20.00 10.66 20.00   8.52   7.78 20.00   7.53
results:NNS 20.00   7.20 14.56 20.50   5.74 10.00 20.00 15.00 12.00 20.00 20.00 14.01 12.00 20.00 13.52 20.00 12.00   6.36 20.00   5.46
their:PRP$ 20.00 12.50 15.00 20.50 12.00 12.00 20.00 15.00   0.00 20.00 20.00 15.00   0.00 20.00 15.00 20.00 15.00 12.00 20.00 12.00
work:NN 20.00   6.60 15.00 19.04   5.04 10.00 20.00 15.00 12.00 20.00 20.00 15.00 12.00 20.00 15.00 20.00 12.00   7.58 20.00   6.55
early:JJ 20.00 12.50 11.93 19.23 12.00 12.00 20.00 12.00 15.00 20.00 20.00 10.03 15.00 20.00 12.00 20.00   9.80   8.84 20.00 10.77
October:NNP 20.50   8.01 15.50 20.50   7.20 10.50 20.50 15.50 12.50 20.50 20.50 15.50 12.50 20.50 15.50 20.50 12.50   9.12 20.50   7.59
around:RB 20.00 15.50 20.00 20.50 15.00 15.00 20.00 20.00 15.00 20.00 20.00 20.00 15.00 20.00 20.00 20.00 12.00 15.00 20.00 15.00
200:CD 20.50 20.50 20.50   0.00 18.13 20.50 20.50 20.02 20.50 20.50 20.50 20.50 20.50 20.50 20.19 20.50 20.50 19.86 20.50 20.50
organizations:NNS 20.00   3.24 13.70 20.50   2.19   6.15 20.00 13.71 12.00 20.00 20.00 14.13 12.00 20.00 13.18 20.00 11.27   7.10 20.00   5.95
individuals:NNS 20.00   5.62 13.14 20.50   3.93 10.00 20.00 12.17 12.00 20.00 20.00 13.51 12.00 20.00 14.26 20.00 12.00   6.01 20.00   5.64
to:TO 10.00 20.50 20.00 20.50 20.00 20.00 17.58 20.00 20.00 20.00   0.00 20.00 20.00 17.78 20.00 10.00 20.00 20.00 10.00 20.00
warn:VB 20.00 15.50   8.03 20.12 15.00 15.00 20.00   8.96 15.00 20.00 20.00   5.52 15.00 20.00   6.43 20.00 12.00 14.30 20.00 13.68
individuals:NNS 20.00   5.62 13.14 20.50   3.93 10.00 20.00 12.17 12.00 20.00 20.00 13.51 12.00 20.00 14.26 20.00 12.00   6.01 20.00   5.64
charges:NNS 20.00   7.89 10.53 19.92   6.55 10.00 20.00   0.00 12.00 20.00 20.00 13.23 12.00 20.00 13.12 20.00 10.23   8.53 20.00   6.38
individuals:NNS 20.00   5.62 13.14 20.50   3.93 10.00 20.00 12.17 12.00 20.00 20.00 13.51 12.00 20.00 14.26 20.00 12.00   6.01 20.00   5.64
to:TO 10.00 20.50 20.00 20.50 20.00 20.00 17.58 20.00 20.00 20.00   0.00 20.00 20.00 17.78 20.00 10.00 20.00 20.00 10.00 20.00
give:VB 20.00 15.50 10.00 19.90 12.74 15.00 20.00   7.98 15.00 20.00 20.00   6.76 15.00 20.00   8.93 20.00 12.00 15.00 20.00 15.00
individuals:NNS 20.00   5.62 13.14 20.50   3.93 10.00 20.00 12.17 12.00 20.00 20.00 13.51 12.00 20.00 14.26 20.00 12.00   6.01 20.00   5.64
the:DT   0.00 20.50 20.00 20.50 20.00 20.00 20.00 20.00 15.71 20.00 10.00 20.00 15.71 20.00 20.00   0.00 20.00 20.00   0.00 20.00
opportunity:NN 20.00   7.51 15.00 20.50   6.11 10.00 20.00 14.39 12.00 20.00 20.00 15.00 12.00 20.00 15.00 20.00 12.00   8.26 20.00   7.40
to:TO 10.00 20.50 20.00 20.50 20.00 20.00 17.58 20.00 20.00 20.00   0.00 20.00 20.00 17.78 20.00 10.00 20.00 20.00 10.00 20.00
respond:VB 20.00 15.50   7.18 20.50 14.29 15.00 20.00   9.32 15.00 20.00 20.00   6.88 15.00 20.00   0.00 20.00   9.12 13.03 20.00 12.21
final:JJ 20.00 12.50 12.00 20.21 12.00 12.00 20.00 11.69 15.00 20.00 20.00 10.51 15.00 20.00 12.00 20.00   9.28   9.13 20.00   9.81
drafting:NNS 20.00 10.50 12.97 20.50 10.00 10.00 20.00 15.00 12.00 20.00 20.00 13.78 12.00 20.00 14.89 20.00 10.58   9.28 20.00   9.78
the:DT   0.00 20.50 20.00 20.50 20.00 20.00 20.00 20.00 15.71 20.00 10.00 20.00 15.71 20.00 20.00   0.00 20.00 20.00   0.00 20.00
report:NN 20.00   7.98 12.54 20.50   6.66 10.00 20.00 15.00 12.00 20.00 20.00 14.36 12.00 20.00 12.94 20.00   8.41   3.16 20.00   0.00
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00   1.00   9.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.77 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  11.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.15 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added release[release-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "drafting" of "respond" dropped on aligned hyp word "responding"
-1.00  1.00 NullPunisher.other : oragnisations
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : prevent
-1.00  1.00 NullPunisher.other : official
-1.00  1.00 NullPunisher.other : in_order
-1.00  1.00 NullPunisher.other : before
-1.00  1.00 NullPunisher.other : them
-1.00  1.00 NullPunisher.other : release
-1.00  1.00 NullPunisher.other : from
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : them
-1.00  1.00 Structure.relMismatch : text "Truth_and_Reconciliation_Commission" is poss of "investigators" while hyp "Commission" is nsubj of "investigated" which aligned to text "investigators"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -9.1593
Threshold: -1.8794


Inference ID: 1182

Txt: This explanation was rejected by the Government of Chile, who insist that Pinochet enjoys diplomatic immunity in his capacity as "an elected member in the Senate for life" since leaving the command of the armed forces last March.

Hyp: The Chilean government affirming that as a member of the Senate, Pinochet has diplomatic immunity. (yes)

The
DT
Chilean
JJ
government
NN
affirming
VBG
that
IN
a
DT
member
NN
the
DT
Senate
NNP
Pinochet
NNP
has
VBZ
diplomatic_immunity
NN
This:DT 10.00 20.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50 20.50 20.00 20.00
explanation:NN 20.00 12.00   6.65 14.99 20.00 20.00   7.27 20.00   8.36 10.50 15.00 10.00
was:VBD 20.00 12.00 15.00 10.00 20.00 20.00 15.00 20.00 15.50 15.50 10.00 15.00
rejected:VBN 20.00 12.00 14.28   6.69 20.00 20.00 12.03 20.00 15.50 15.50 10.00 15.00
the:DT   0.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.50 20.50 20.00 20.00
Government:NN 20.00 12.00   0.00 15.00 20.00 20.00   6.73 20.00   6.27 10.50 15.00 10.00
Chile:NNP 20.50   5.50   9.16 15.50 20.50 20.50   9.18 20.50   9.82 10.50 15.50 10.50
who:WP 20.00 15.00 12.00 15.00 20.00 20.00 12.00 20.00 12.50 12.50 15.00 12.00
insist:VBP 20.00 12.00 13.35 10.00 20.00 20.00 14.80 20.00 15.50 15.50 10.00 15.00
that:IN 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.50 20.50 20.00 20.00
Pinochet:NNP 20.50 12.50 10.50 15.50 20.50 20.50 10.50 20.50 10.50   0.00 15.50 10.50
enjoys:VBZ 20.00 12.00 15.00   8.22 20.00 20.00 15.00 20.00 15.50 15.50 10.00 15.00
diplomatic_immunity:NN 20.00 12.00 10.00 15.00 20.00 20.00 10.00 20.00 10.50 10.50 15.00   0.00
his:PRP$ 20.00 15.00 12.00 15.00 20.00 20.00 12.00 20.00 12.50 12.50 15.00 12.00
capacity:NN 20.00 12.00   7.74 15.00 20.00 20.00   8.23 20.00   9.18 10.50 15.00 10.00
an:DT 10.00 20.00 20.00 20.00 20.00   8.73 20.00 10.00 20.50 20.50 20.00 20.00
elected:VBN 20.00 12.00 12.70   9.49 20.00 20.00 12.65 20.00 15.50 15.50 10.00 15.00
member:NN 20.00 12.00   6.73 15.00 20.00 20.00   0.00 20.00   8.42 10.50 15.00 10.00
the:DT   0.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.50 20.50 20.00 20.00
Senate:NNP 20.50 12.50   6.27 15.50 20.50 20.50   8.42 20.50   0.00 10.50 15.50 10.50
life:NN 20.00 12.00   7.29 12.62 20.00 20.00   7.84 20.00   8.84 10.50 15.00 10.00
since:IN 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.50 20.50 20.00 20.00
leaving:VBG 20.00 12.00 14.77 10.00 20.00 20.00 13.79 20.00 15.50 15.50 10.00 15.00
the:DT   0.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.50 20.50 20.00 20.00
command:NN 20.00 12.00   6.52 15.00 20.00 20.00   7.16 20.00   8.26 10.50 15.00 10.00
the:DT   0.00 20.00 20.00 20.00 20.00 10.00 20.00   0.00 20.50 20.50 20.00 20.00
armed:JJ 20.00 10.00   9.62 12.00 20.00 20.00   9.47 20.00 12.50 12.50 12.00 12.00
forces:NNS 20.00 12.00   3.45 15.00 20.00 20.00   6.68 20.00   6.21 10.50 15.00 10.00
last:JJ 20.00 10.00 12.00 12.00 20.00 20.00 12.00 20.00 12.50 12.50 12.00 12.00
March:NNP 20.50 12.50   8.03 15.50 20.50 20.50   8.54 20.50   9.02 10.50 15.50 10.50
NO_WORD   1.00   9.00 10.00 10.00   1.00   1.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.25 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added affirming[affirming-VBG]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "capacity" of "diplomatic_immunity" dropped on aligned hyp word "diplomatic_immunity"
-1.00  1.00 NullPunisher.other : Chilean
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : affirming
 1.00  1.00 Quant.contract : [an,a]
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.3924
Threshold: -1.8794


Inference ID: 2105

Txt: Brazil, Australia and Thailand argued that the EU, one of the world's largest exporters, broke WTO farm rules by exceeding limits on export subsidies laid down under WTO's 1994 Agreement on Agriculture.

Hyp: The three countries argued that UE, one of the greater exporter of the world, infringed the rules of the OMC when exceeding the limit of the subsidies to the exports indicated under the Agreement on Agriculture of the OMC in 1994. (don't know)

The
DT
three
CD
countries
NNS
argued
VBD
that
IN
UE
NNP
one
CD
the
DT
greater
JJR
exporter
NN
the
DT
world
NN
infringed
VBD
the
DT
rules
NNS
the
DT
OMC
NNP
when
WRB
exceeding
VBG
the
DT
limit
NN
the
DT
subsidies
NNS
the
DT
exports
NNS
indicated
VBD
the
DT
Agreement
NN
Agriculture
NNP
the
DT
OMC
NNP
1994
CD
Brazil:NNP 20.50 20.50   4.63 15.50 20.50 10.50 20.50 20.50 12.50   9.77 20.50   9.26 15.50 20.50   9.32 20.50 10.50 20.50 15.50 20.50   9.60 20.50   9.83 20.50   9.26 15.50 20.50   8.72   9.89 20.50 10.50 20.50
Australia:NNP 20.50 20.50   4.01 15.50 20.50 10.50 20.50 20.50 12.50   9.56 20.50   8.96 15.50 20.50   9.04 20.50 10.50 20.50 15.50 20.50   9.35 20.50   9.62 20.50   8.97 15.50 20.50   8.35   9.69 20.50 10.50 20.50
Thailand:NNP 20.50 20.50   5.43 15.50 20.50 10.50 20.50 20.50 12.50   9.88 20.50   9.67 15.50 20.50   9.78 20.50 10.50 20.50 15.50 20.50   9.98 20.50 10.14 20.50   9.73 15.50 20.50   9.31 10.18 20.50 10.50 20.50
argued:VBD 20.00 20.50 14.21   0.00 20.00 15.50 20.50 20.00 10.29 14.92 20.00 15.00   6.36 20.00 12.59 20.00 15.50 20.00   8.98 20.00 13.12 20.00 12.72 20.00 14.06   7.52 20.00 15.00 15.00 20.00 15.50 18.52
that:IN 20.00 20.50 20.00 20.00   0.00 20.50 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.50
the:DT   0.00 20.50 20.00 20.00 20.00 20.50 20.50   0.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00   0.00 20.50 10.00 20.00   0.00 20.00   0.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00   0.00 20.50 20.50
EU:NNP 20.50 20.50 10.50 15.50 20.50 10.00 20.50 20.50 12.50 10.50 20.50 10.50 15.50 20.50 10.50 20.50 10.00 20.50 15.50 20.50 10.50 20.50 10.50 20.50 10.50 15.50 20.50 10.50 10.50 20.50 10.00 20.50
one:CD 20.50   5.00 20.50 20.50 20.50 20.50   0.00 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 10.50
the:DT   0.00 20.50 20.00 20.00 20.00 20.50 20.50   0.00 20.00 20.00   0.00 20.00 20.00   0.00 20.00   0.00 20.50 10.00 20.00   0.00 20.00   0.00 20.00   0.00 20.00 20.00   0.00 20.00 20.00   0.00 20.50 20.50
world:NN 20.00 20.50   5.79 15.00 20.00 10.50 20.50 20.00 10.26   4.88 20.00   0.00 12.29 20.00   7.91 20.00 10.50 20.00 15.00 20.00   8.31 20.00   8.65 20.00   7.35 15.00 20.00   7.08   8.74 20.00 10.50 19.76
largest:JJS 20.00 20.49   9.77 11.74 20.00 12.50 20.50 20.00   9.78   6.33 20.00   5.56   8.71 20.00 12.00 20.00 12.50 20.00 11.20 20.00 11.36 20.00 11.88 20.00   9.34 12.00 20.00 12.00 12.00 20.00 12.50 20.03
exporters:NNS 20.00 20.50   7.78 14.44 20.00 10.50 20.50 20.00 12.00   0.00 20.00   8.91 14.52 20.00   8.97 20.00 10.50 20.00 14.20 20.00   9.23 20.00   9.01 20.00   1.00 15.00 20.00   8.41   9.49 20.00 10.50 20.50
broke:VBD 20.00 18.77 15.00   9.44 20.00 15.50 20.50 20.00 12.00 15.00 20.00 13.37   7.70 20.00 13.95 20.00 15.50 20.00 10.00 20.00 13.72 20.00 14.17 20.00 15.00 10.00 20.00 15.00 15.00 20.00 15.50 18.65
WTO:NNP 20.50 20.50 10.50 15.50 20.50 10.00 20.50 20.50 12.50 10.50 20.50 10.50 15.50 20.50 10.50 20.50 10.00 20.50 15.50 20.50 10.50 20.50 10.50 20.50 10.50 15.50 20.50 10.50 10.50 20.50 10.00 20.50
farm:NN 20.00 20.26   5.41 14.81 20.00 10.50 20.50 20.00 11.97   8.94 20.00   8.34 14.97 20.00   8.17 20.00 10.50 20.00 15.00 20.00   5.84 20.00   4.77 20.00   7.83 15.00 20.00   7.70   9.11 20.00 10.50 20.42
rules:NNS 20.00 19.25   6.94 12.59 20.00 10.50 20.50 20.00 10.22   8.97 20.00   7.91 14.08 20.00   0.00 20.00 10.50 20.00 15.00 20.00   4.00 20.00   7.41 20.00   8.35 15.00 20.00   7.19   8.80 20.00 10.50 20.24
by:IN 20.00 20.50 20.00 20.00 10.00 20.50 20.50 18.04 20.00 20.00 18.04 20.00 20.00 18.04 20.00 18.04 20.50 20.00 20.00 18.04 20.00 18.04 20.00 18.04 20.00 20.00 18.04 20.00 20.00 18.04 20.50 20.50
exceeding:VBG 20.00 19.62 14.06   8.98 20.00 15.50 20.50 20.00 11.46 14.35 20.00 15.00   9.59 20.00 15.00 20.00 15.50 20.00   0.00 20.00 14.47 20.00 14.98 20.00 14.16   8.01 20.00 15.00 15.00 20.00 15.50 18.90
limits:NNS 20.00 19.77   7.45 11.62 20.00 10.50 20.50 20.00 10.76   9.23 20.00   8.31 12.05 20.00   3.58 20.00 10.50 20.00 14.67 20.00   0.00 20.00   6.62 20.00   8.70 15.00 20.00   6.86   9.08 20.00 10.50 20.50
export:NN 20.00 20.50   5.62 14.17 20.00 10.50 20.50 20.00 11.35   1.00 20.00   7.10 14.30 20.00   8.35 20.00 10.50 20.00 14.86 20.00   8.69 20.00   7.86 20.00   0.00 15.00 20.00   7.62   9.06 20.00 10.50 20.10
subsidies:NNS 20.00 20.45   7.89 12.72 20.00 10.50 20.50 20.00 10.98   8.71 20.00   8.65 15.00 20.00   7.41 20.00 10.50 20.00 14.98 20.00   5.46 20.00   0.00 20.00   8.43 15.00 20.00   8.09   9.32 20.00 10.50 20.50
laid_down:VBN 20.00 20.50 15.00 10.00 20.00 15.50 20.50 20.00 12.00 15.00 20.00 15.00 10.00 20.00 15.00 20.00 15.50 20.00 10.00 20.00 15.00 20.00 15.00 20.00 15.00 10.00 20.00 15.00 15.00 20.00 15.50 20.50
WTO:NNP 20.50 20.50 10.50 15.50 20.50 10.00 20.50 20.50 12.50 10.50 20.50 10.50 15.50 20.50 10.50 20.50 10.00 20.50 15.50 20.50 10.50 20.50 10.50 20.50 10.50 15.50 20.50 10.50 10.50 20.50 10.00 20.50
1994:CD 20.50 10.50 20.50 18.52 20.50 20.50 10.50 20.50 20.34 20.50 20.50 19.76 19.08 20.50 20.24 20.50 20.50 20.50 18.90 20.50 20.50 20.50 20.50 20.50 18.62 19.46 20.50 20.50 20.50 20.50 20.50   0.00
Agreement:NN 20.00 20.50   5.91 15.00 20.00 10.50 20.50 20.00 12.00   8.41 20.00   7.08 15.00 20.00   7.19 20.00 10.50 20.00 15.00 20.00   6.86 20.00   8.09 20.00   7.62 15.00 20.00   0.00   8.19 20.00 10.50 20.50
Agriculture:NNP 20.00 20.50   8.00 15.00 20.00 10.50 20.50 20.00 12.00   9.49 20.00   8.74 15.00 20.00   8.80 20.00 10.50 20.00 15.00 20.00   9.08 20.00   9.32 20.00   9.06 15.00 20.00   8.19   0.00 20.00 10.50 20.50
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00   9.00 10.00   1.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.87 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  14.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added OMC[OMC-NNP]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "largest" of "exporters" dropped on aligned hyp word "exports"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1994
 1.00  1.00 Hypernym.posWiden : widening in positive context: Australia -> country
-3.00  1.00 NullPunisher.entity : three
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : when
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : indicated
-1.00  1.00 NullPunisher.other : greater
-3.00  1.00 NullPunisher.entity : OMC
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : The
 1.00  1.00 Quant.contract : [the,one]
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -6.5347
Threshold: -1.8794


Inference ID: 1162

Txt: UNSCOM employs about 120 persons in Iraq, including 40 inspectors.

Hyp: Wilson uses some 120 persons in Iraq, 40 of whom are inspectors. (don't know)

Wilson
NNP
uses
VBZ
some
DT
120
CD
persons
NNS
Iraq
NNP
40
CD
of
IN
whom
WP
are
VBP
inspectors
NNS
UNSCOM:NNP 10.50 15.50 20.50 20.50 10.50   9.33 20.50 20.50 12.50 15.50 10.50
employs:VBZ 15.50   9.88 20.00 18.73 12.10 15.50 20.50 20.00 15.00 10.00 12.41
about:RB 15.50 20.00 20.00 20.50 15.00 15.50 20.50 18.63 15.00 20.00 15.00
120:CD 20.50 20.50 20.50   0.00 20.50 20.50   5.00 20.50 20.50 20.50 20.40
persons:NNS   6.45 15.00 20.00 20.50   0.00   7.80 20.50 20.00 12.00 15.00   6.02
Iraq:NNP   9.87 15.50 20.50 20.50   7.80   0.00 20.50 20.50 12.50 15.50   9.88
including:VBG 15.50   7.11 20.00 20.50 12.71 15.50 20.50 20.00 15.00 10.00 13.71
40:CD 20.50 20.50 20.50   5.00 20.50 20.50   0.00 20.50 20.50 20.50 20.50
inspectors:NNS   9.00 12.85 20.00 20.40   6.02   9.88 20.50 20.00 12.00 15.00   0.00
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.18 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.36 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "about" of "120" dropped on aligned hyp word "120"
-1.00  1.00 NullPunisher.other : of
-1.00  1.00 NullPunisher.other : some
-3.00  1.00 NullPunisher.entity : Wilson
-1.00  1.00 NullPunisher.other : whom
-0.05  1.00 NullPunisher.aux : are
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "uses" aligned badly to "employs"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -5.0352
Threshold: -1.8794


Inference ID: 1173

Txt: The poll also showed that a higher percentage of Americans (65%) believe that even if the investigation proceedings actually began, Clinton ought not to be removed.

Hyp: According to the poll, 65% of Americans believe that Clinton should be removed from office. (don't know)

the
DT
poll
NN
65
CD
%
NN
Americans
NNP
believe
VBP
that
IN
Clinton
NNP
should
MD
be
VB
removed
VBN
office
NN
The:DT   0.00 20.00 20.50 20.50 20.50 20.00 20.00 20.50 10.00 20.00 20.00 20.00
poll:NN 20.00   0.00 17.74 10.50   9.14 13.60 20.00 10.07 20.00 15.00 15.00   8.76
also:RB 20.00 15.00 20.50 15.50 15.50 20.00 20.00 15.50 20.00 20.00 20.00 15.00
showed:VBD 20.00 10.97 20.50 15.50 15.50   9.71 20.00 15.50 20.00 10.00 10.00 13.07
that:IN 20.00 20.00 20.50 20.50 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00
a:DT 10.00 20.00 20.50 20.50 20.50 20.00 20.00 20.50 10.00 20.00 20.00 20.00
higher:JJR 20.00 12.00 20.48 12.13 12.50 10.34 20.00 12.50 20.00 12.00 12.00 12.00
percentage:NN 20.00   7.83 20.16   7.94   8.61 14.13 20.00   9.79 20.00 15.00 15.00   8.27
Americans:NNS 20.00   8.64 20.50 10.50   0.50 15.00 20.00   8.58 20.00 15.00 15.00   6.98
65:CD 20.50 17.74   0.00 20.00 20.50 19.50 20.50 20.50 20.50 20.50 19.95 20.50
%:NN 20.50 10.50 20.00   0.00 10.50 15.32 20.50 10.50 20.50 15.50 15.50 10.50
believe:VBP 20.00 13.60 19.50 15.32 15.50   0.00 20.00 15.50 20.00 10.00 10.00 15.00
that:IN 20.00 20.00 20.50 20.50 20.50 20.00   0.00 20.50 20.00 20.00 20.00 20.00
even:RB 20.00 15.00 20.50 15.50 15.50 20.00 20.00 15.50 20.00 20.00 20.00 15.00
if:IN 17.77 20.00 20.50 20.50 20.50 20.00 10.00 20.50 20.00 20.00 20.00 20.00
the:DT   0.00 20.00 20.50 20.50 20.50 20.00 20.00 20.50 10.00 20.00 20.00 20.00
investigation:NN 20.00   3.03 20.50 10.24   8.70 13.79 20.00   9.84 20.00 15.00 11.67   4.76
proceedings:NNS 20.00   8.44 20.50 10.13   7.70 15.00 20.00   9.26 20.00 15.00 11.77   7.40
actually:RB 20.00 14.15 19.97 14.53 15.50 16.70 20.00 15.50 20.00 20.00 20.00 14.27
began:VBD 20.00 15.00 20.50 14.58 15.50 10.00 20.00 15.50 20.00 10.00   7.16 14.36
Clinton:NNP 20.50 10.07 20.50 10.50   8.58 15.50 20.50   0.00 20.50 15.50 15.50   9.12
ought:MD 10.00 18.35 19.53 20.16 20.50 16.26 20.00 20.50 10.00 20.00 20.00 20.00
not:RB 20.00 15.00 20.50 15.50 15.50 20.00 20.00 15.50 20.00 20.00 20.00 15.00
to:TO 10.00 20.00 20.50 20.50 20.50 20.00 20.00 20.50 10.00 20.00 20.00 20.00
be:VB 20.00 15.00 20.50 15.50 15.50 10.00 20.00 15.50 20.00   0.00 10.00 15.00
removed:VBN 20.00 15.00 19.95 15.50 15.50 10.00 20.00 15.50 20.00 10.00   0.00 12.85
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.34 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added office[office-NN]
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
 0.00  1.00 NegPolarity.txtNegWord : "removed": has child with relation "neg"
-1.00  1.00 NullPunisher.other : office
-0.05  1.00 NullPunisher.aux : should
-0.10  1.00 NullPunisher.functionWord : that
-3.00  1.00 Structure.argsMismatch : mismatched args with same relation ccomp args have different parents, different relations: text "poll" <-nsubj-- "showed" vs. hyp "poll" <-prep_according_to-- "believe", which aligned to text "believe" args have different parents, different relations: text "%" <-dep-- "Americans" vs. hyp "%" <-nsubj-- "believe", which aligned to text "believe" args have different parents, different relations: text "%" <-prep_of-- "percentage" vs. hyp "%" <-nsubj-- "believe", which aligned to text "believe" args have different parents, different relations: text "removed" <-xcomp-- "ought" vs. hyp "removed" <-ccomp-- "believe", which aligned to text "believe"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.5610
Threshold: -1.8794


Inference ID: 1232

Txt: The Damascus public have been welcomed into the election candidate's private homes or to specially erected tents where voters have been enjoying food, drink and entertainment, particularly in the home of the wealthy candidate, Adnan Mullah, where dozens of supporters and friends have been invited to dinner every evening.

Hyp: Candidates are entertaining voters in their homes. (yes)

Candidates
NNS
are
VBP
entertaining
JJ
voters
NNS
their
PRP$
homes
NNS
The:DT 20.00 20.00 20.00 20.00 20.00 20.00
Damascus:NNP   9.84 15.50 12.50   9.85 12.50   8.12
public:NN   8.46 15.00 12.00   7.93 12.00   7.84
have:VBP 15.00 10.00 12.00 15.00 15.00 15.00
been:VBN 15.00   0.00 12.00 15.00 15.00 15.00
welcomed:VBN 15.00 10.00 12.00 14.92 15.00 15.00
into:IN 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.00 20.00 20.00 20.00 20.00 20.00
election:NN   8.85 15.00 12.00   3.43 12.00   8.32
candidate:NN   0.00 15.00 12.00   2.43 12.00   8.04
private:JJ 12.00 12.00 10.00 12.00 15.00 11.00
homes:NNS   8.04 15.00 11.64   8.07 12.00   0.00
to:TO 20.00 20.00 20.00 20.00 20.00 20.00
specially:RB 15.00 20.00 11.32 15.00 15.00 14.42
erected:JJ 12.00 12.00 10.00 12.00 15.00 10.20
tents:NNS   8.58 15.00 11.65   8.61 12.00   6.89
where:WRB 20.00 20.00 20.00 20.00 20.00 20.00
voters:NNS   8.14 15.00 12.00   0.00 12.00   8.07
have:VBP 15.00 10.00 12.00 15.00 15.00 15.00
been:VBN 15.00   0.00 12.00 15.00 15.00 15.00
enjoying:VBG 15.00 10.00   9.24 14.30 15.00 13.11
food:NN   7.15 15.00 12.00   7.20 12.00   6.28
drink:NN   9.10 15.00 12.00   9.12 12.00   7.72
entertainment:NN   8.01 15.00   3.00   8.05 12.00   7.30
particularly:RB 15.00 20.00   9.99 13.87 15.00 13.25
the:DT 20.00 20.00 20.00 20.00 20.00 20.00
home:NN   8.04 15.00 11.90   8.07 12.00   0.00
the:DT 20.00 20.00 20.00 20.00 20.00 20.00
wealthy:JJ 12.00 12.00   9.22 10.25 15.00 10.56
candidate:NN   0.00 15.00 12.00   2.43 12.00   8.04
Adnan_Mullah:NNP 10.50 15.50 12.50 10.50 12.50 10.50
where:WRB 20.00 20.00 20.00 20.00 20.00 20.00
dozens:NNS   8.72 15.00 10.33   8.74 12.00   8.16
supporters:NNS   7.69 15.00 12.00   2.64 12.00   7.61
friends:NNS   7.33 15.00 10.49   7.37 12.00   6.83
have:VBP 15.00 10.00 12.00 15.00 15.00 15.00
been:VBN 15.00   0.00 12.00 15.00 15.00 15.00
invited:VBN 15.00 10.00 11.93 14.87 15.00 14.93
dinner:NN   9.02 15.00 10.50   9.04 12.00   7.80
every:DT 20.00 20.00 20.00 20.00 20.00 20.00
evening:NN   9.19 15.00   9.23   9.21 12.00   8.76
NO_WORD 10.00   1.00   9.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "private" of "homes" dropped on aligned hyp word "homes"
-0.10  1.00 NullPunisher.functionWord : their
-0.05  1.00 NullPunisher.aux : are
-1.00  1.00 Structure.relMismatch : noun args have different parents but same relations: "entertaining": "food" vs. "voters"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.1979
Threshold: -1.8794


Inference ID: 2104

Txt: It could mean to leave in the street to thousands of workers.

Hyp: It could lead to thousands of job losses. (don't know)

It
PRP
could
MD
lead
VB
thousands
NNS
job
NN
losses
NNS
It:PRP   0.00 20.00 15.00 12.00 12.00 12.00
could:MD 20.00   0.00 20.00 20.00 20.00 20.00
mean:VB 15.00 20.00   9.62 14.86 13.87 14.40
to:TO 20.00 10.00 20.00 20.00 20.00 20.00
leave:VB 15.00 20.00   5.00 13.39 11.32 15.00
the:DT 20.00 10.00 20.00 20.00 20.00 20.00
street:NN 12.00 20.00 14.64   8.19   6.90   8.19
thousands:NNS 12.00 20.00 15.00   0.00   7.06   8.00
workers:NNS 12.00 20.00 14.96   5.94   4.68   7.02
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.83 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added losses[losses-NNS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "workers" of "thousands" dropped on aligned hyp word "thousands"
 2.00  1.00 Modal.yes : actual -> possible
-1.00  1.00 NullPunisher.other : losses
-0.05  1.00 NullPunisher.aux : could
-1.00  1.00 NullPunisher.other : job
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "lead" aligned badly to "leave"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.4449
Threshold: -1.8794


Inference ID: 1244

Txt: The source added that the investigation proved that the bases of the genocide crime "were completed with a series of illegal arrests followed in some cases with assassinations or cases of disappearances and were preceded, according to information attached to the file, by cases of torture."

Hyp: Investigators discovered that a series of illicit arrests of were often followed by disappearances or murders and were preceded by torture. (yes)

Investigators
NNS
discovered
VBD
that
IN
a
DT
series
NN
illicit
JJ
arrests
NNS
of
IN
were
VBD
often
RB
followed
VBN
disappearances
NNS
murders
NNS
were
VBD
preceded
VBN
torture
NNP
The:DT 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
source:NN   7.76 13.52 20.00 20.00   7.14 10.93   8.58 20.00 15.00 15.00 14.87   9.00   8.05 15.00 14.52   8.82
added:VBD 15.00   9.16 20.00 20.00 15.00 12.00 13.35 20.00 10.00 20.00 10.00 14.54 14.32 10.00 10.00 15.00
that:IN 20.00 20.00   0.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.00 20.00 20.00 10.00 20.00 20.00 20.00 18.70 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
investigation:NN   1.00 11.16 20.00 20.00   7.91   7.48   4.77 20.00 15.00 15.00 14.36   7.27   6.51 15.00 13.52   6.62
proved:VBD 15.00   5.96 20.00 20.00 12.75   9.88 13.91 20.00 10.00 20.00   6.98 14.73 13.28 10.00   6.12 13.31
that:IN 20.00 20.00   0.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.00 20.00 20.00 10.00 20.00 20.00 20.00 18.70 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
bases:NNS   7.97 15.00 20.00 20.00   7.39 10.24   8.49 20.00 15.00 15.00 13.09   7.09   8.24 15.00 15.00   8.62
the:DT 20.00 20.00 20.00 10.00 20.00 20.00 20.00 18.70 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
genocide:NN   9.49 14.26 20.00 20.00   8.93   9.74   7.01 20.00 15.00 15.00 14.69   5.12   2.69 15.00 13.09   3.00
crime:NN   8.11 14.43 20.00 20.00   7.00   9.58   3.86 20.00 15.00 15.00 15.00   6.66   3.25 15.00 13.66   7.48
were:VBD 15.00 10.00 20.00 20.00 15.00 12.00 15.00 20.00   0.00 20.00 10.00 15.00 15.00   0.00 10.00 15.00
completed:VBN 15.00   7.54 20.00 20.00 13.78 12.00 15.00 20.00 10.00 20.00   7.85 15.00 14.55 10.00   8.06 15.00
a:DT 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
series:NN   7.92 14.62 20.00 20.00   0.00 12.00   8.32 20.00 15.00 15.00 12.56   8.79   7.74 15.00 12.06   9.09
illegal:JJ 12.00 11.35 20.00 20.00 12.00   5.01   8.69 20.00 12.00 12.00 12.00 10.23   9.72 12.00 12.00   9.31
arrests:NNS   9.07 13.72 20.00 20.00   8.32   8.12   0.00 20.00 15.00 15.00 14.33   4.43   2.23 15.00 14.27   5.90
followed:VBN 15.00   7.78 20.00 20.00 12.56 11.83 14.33 20.00 10.00 20.00   0.00 14.56 12.67 10.00   4.89 14.31
some:DT 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
cases:NNS   8.24 14.25 20.00 20.00   7.17   9.33   5.20 20.00 15.00 15.00 14.62   7.48   4.50 15.00 14.81   6.36
assassinations:NNS   9.17 14.01 20.00 20.00   8.46   8.71   4.98 20.00 15.00 15.00 14.52   3.11   5.08 15.00 12.10   3.73
cases:NNS   8.24 14.25 20.00 20.00   7.17   9.33   5.20 20.00 15.00 15.00 14.62   7.48   4.50 15.00 14.81   6.36
disappearances:NNS   9.40 15.00 20.00 20.00   8.79   9.51   4.43 20.00 15.00 15.00 14.56   0.00   5.24 15.00 13.25   3.83
were:VBD 15.00 10.00 20.00 20.00 15.00 12.00 15.00 20.00   0.00 20.00 10.00 15.00 15.00   0.00 10.00 15.00
preceded:VBN 15.00   7.75 20.00 20.00 12.06 10.95 14.27 20.00 10.00 20.00   4.89 13.25 12.28 10.00   0.00 12.47
information:NN   7.58 13.30 20.00 20.00   6.30 10.63   8.01 20.00 15.00 15.00 15.00   8.54   7.38 15.00 15.00   8.53
attached:VBN 15.00   8.35 20.00 20.00 14.34 11.29 15.00 20.00 10.00 20.00 10.00 15.00 15.00 10.00   9.37 13.24
the:DT 20.00 20.00 20.00 10.00 20.00 20.00 20.00 18.70 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
file:NN   8.88 14.29 20.00 20.00   8.05 10.62   9.15 20.00 15.00 15.00 14.66   9.46   8.76 15.00 15.00   9.63
cases:NNS   8.24 14.25 20.00 20.00   7.17   9.33   5.20 20.00 15.00 15.00 14.62   7.48   4.50 15.00 14.81   6.36
torture:NNP   9.59 12.08 20.00 20.00   9.09   8.50   5.90 20.00 15.00 15.00 14.31   3.83   5.94 15.00 12.47   0.00
NO_WORD 10.00 10.00   1.00   1.00 10.00   9.00 10.00 10.00   1.00   9.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.12 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.31 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added often[often-RB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "information" of "preceded" dropped on aligned hyp word "preceded"
-0.05  1.00 NullPunisher.aux : were
-1.00  1.00 NullPunisher.other : often
-1.00  1.00 NullPunisher.other : murders
-1.00  1.00 NullPunisher.other : of
-1.00  1.00 NullPunisher.other : discovered
-0.10  1.00 NullPunisher.functionWord : that
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "discovered" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.3015
Threshold: -1.8794


Inference ID: 1178

Txt: Sources in Tegucigalpa reported that many Latin American countries are gearing up to face Hurricane "Mitch".

Hyp: South American countries are preparing for "Mitch" (yes)

South_American_countries
NNS
are
VBP
preparing
VBG
Mitch
NNP
Sources:NNS   7.20 15.00 15.00 10.00
Tegucigalpa:NNP   7.13 15.50 15.50 10.50
reported:VBD 15.50 10.00   9.39 15.00
that:IN 20.50 20.00 20.00 20.00
many:JJ 12.50 12.00 12.00 12.00
Latin_American:NNS 10.50 15.00 15.00 10.00
countries:NNS   0.50 15.00 15.00 10.00
are:VBP 15.50   0.00 10.00 15.00
gearing_up:VBG 15.50 10.00 10.00 15.00
to:TO 20.50 20.00 20.00 20.00
face:VB 15.50 10.00 10.00 15.00
Hurricane:NNP   9.46 15.00 15.00 10.00
Mitch:NNP 10.50 15.00 15.00   0.00
NO_WORD 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.86 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Latin_American" of "countries" dropped on aligned hyp word "South_American_countries"
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: reported-VBD
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: country -> south_american_countries
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "preparing" aligned badly to "gearing_up"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Mitch" <-dep-- "face" vs. hyp "Mitch" <-prep_for-- "preparing", which aligned to text "gearing_up"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.3034
Threshold: -1.8794


Inference ID: 1199

Txt: Clinton informed the two parties that in the absence of an agreement, a public announcement would be made, with the apparent objective of applying pressure to both sides, particularly the Israelis.

Hyp: Clinton told the two sides that he would publicly announce their failure to agree, thereby pressurising them. (yes)

Clinton
NNP
told
VBD
the
DT
two
CD
sides
NNS
that
IN
he
PRP
would
MD
publicly
RB
announce
VB
their
PRP$
failure
NN
to
TO
agree
VB
thereby
RB
pressurizing
VBG
their
PRP$
Clinton:NNP   0.00 15.50 20.50 20.50   9.42 20.50 12.50 20.50 15.50 15.50 12.50   9.50 20.50 15.50 15.50 15.50 12.50
informed:VBD 15.50   4.53 20.00 20.28 13.60 20.00 15.00 20.00 15.47   6.60 15.00 12.90 20.00   9.47 20.00 10.00 15.00
the:DT 20.50 20.00   0.00 20.50 20.00 20.00 18.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
two:CD 20.50 19.88 20.50   0.00 18.32 20.50 20.50 20.50 18.60 20.38 18.43 20.20 18.50 19.28 20.50 20.50 18.43
parties:NNS   9.54 15.00 20.00 19.86   5.03 20.00 12.00 20.00 14.10 13.80 12.00   7.78 20.00 12.75 15.00 15.00 12.00
that:IN 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.50 20.00   0.00 20.50 20.00 20.00 18.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
absence:NN 10.11 15.00 20.00 18.90   8.77 20.00 12.00 20.00 13.44 14.68 12.00   4.77 20.00 13.51 15.00 15.00 12.00
an:DT 20.50 20.00 10.00 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00   8.20 20.00 20.00 20.00 20.00
agreement:NN   9.06 13.67 20.00 19.46   4.41 20.00 12.00 20.00 12.27 11.51 12.00   7.01 20.00   2.50 15.00 15.00 12.00
a:DT 20.50 20.00 10.00 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
public:JJ 12.50 11.70 20.00 20.50 12.00 20.00 15.00 20.00   2.50 11.91 15.00 11.01 20.00 11.36 12.00 12.00 15.00
announcement:NN   9.83 12.32 20.00 19.65   8.23 20.00 12.00 20.00 10.77   1.00 12.00   8.26 20.00 14.52 15.00 15.00 12.00
would:MD 20.50 20.00 10.00 20.50 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
be:VB 15.50 10.00 20.00 20.50 15.00 20.00 15.00 18.78 20.00 10.00 15.00 15.00 20.00 10.00 20.00 10.00 15.00
made:VBN 15.50   7.71 20.00 17.85 12.85 20.00 15.00 20.00 16.19   9.08 15.00 12.49 20.00 10.00 20.00 10.00 15.00
the:DT 20.50 20.00   0.00 20.50 20.00 20.00 18.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
apparent:JJ 12.50 11.35 20.00 19.57 10.66 20.00 15.00 20.00   8.86 10.79 15.00   6.89 20.00 10.92 12.00 12.00 15.00
objective:NN   9.21 15.00 20.00 20.50   7.64 20.00 12.00 20.00 13.21 15.00 12.00   7.25 20.00 13.37 15.00 15.00 12.00
of:IN 20.50 20.00 18.70 20.50 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 18.59 20.00 20.00 20.00 20.00
applying:VBG 15.50   9.96 20.00 20.48 14.42 20.00 15.00 20.00 20.00   9.92 15.00 15.00 20.00 10.00 20.00 10.00 15.00
pressure:NN   9.67 14.64 20.00 20.50   8.31 20.00 12.00 20.00 14.38 15.00 12.00   6.91 20.00 11.72 15.00   6.00 12.00
both:DT 20.50 20.00 10.00 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
sides:NNS   9.42 14.72 20.00 18.32   0.00 20.00 12.00 20.00 13.86 12.34 12.00   8.05 20.00 10.51 15.00 15.00 12.00
particularly:RB 15.50 20.00 20.00 19.91 14.75 20.00 15.00 20.00   7.99 20.00 15.00 12.96 20.00 18.25 10.00 20.00 15.00
the:DT 20.50 20.00   0.00 20.50 20.00 20.00 18.00 10.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
Israelis:NNP   9.07 15.50 20.50 20.50   9.43 20.50 12.50 20.50 15.50 15.50 12.50   9.50 20.50 15.50 15.50 15.50 12.50
NO_WORD 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00   9.00 10.00 10.00 10.00 10.00 10.00   9.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.89 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  10.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.12 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added thereby[thereby-RB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "particularly" of "sides" dropped on aligned hyp word "sides"
-0.10  1.00 NullPunisher.functionWord : that
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : he
-1.00  1.00 NullPunisher.other : thereby
-1.00  1.00 NullPunisher.other : told
-1.00  1.00 NullPunisher.other : failure
-0.05  1.00 NullPunisher.aux : would
-0.10  1.00 NullPunisher.functionWord : their
-0.10  1.00 NullPunisher.functionWord : their
-2.00  1.00 RootEntailment.unalignedRoot : "told" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -5.5815
Threshold: -1.8794


Inference ID: 1158

Txt: An official of the International Special Commission in charge of disarming Iraqi weapons (UNSCOM) announced yesterday, Saturday, that the Commission will keep its inspectors in Iraq despite Baghdad's decision to stop its cooperation with them.

Hyp: UNSCOM is to stay in Iraq. (yes)

UNSCOM
NNP
is
VBZ
to
TO
stay
VB
Iraq
NNP
An:DT 20.50 20.00 10.00 20.00 20.50
official:NN 10.50 15.00 20.00 15.00   8.95
the:DT 20.50 20.00 10.00 20.00 20.50
International_Special_Commission:NNP 10.00 15.50 20.50 15.50   9.79
charge:NN 10.50 15.00 20.00 14.84   9.62
disarming:VBG 15.50 10.00 20.00 10.00 15.50
Iraqi:NNP   9.87 15.50 20.50 15.50   3.00
weapons:NNS 10.50 15.00 20.00 15.00   9.29
UNSCOM:NNP   0.00 15.50 20.50 15.50   9.33
announced:VBD 15.50 10.00 20.00   8.51 15.50
yesterday:NN 10.50 15.00 20.00 15.00   9.62
Saturday:NNP 10.50 15.50 20.50 15.50 10.08
that:IN 20.50 20.00 20.00 20.00 20.50
the:DT 20.50 20.00 10.00 20.00 20.50
Commission:NNP 10.00 15.50 20.50 15.50   9.47
will:MD 20.50 20.00 10.00 20.00 20.50
keep:VB 15.50 10.00 20.00   4.33 15.50
its:PRP$ 12.50 13.00 20.00 15.00 12.50
inspectors:NNS 10.50 15.00 20.00 15.00   9.88
Iraq:NNP   9.33 15.50 20.50 15.50   0.00
Baghdad:NNP   9.44 15.50 20.50 15.50   6.98
decision:NN 10.50 15.00 20.00 12.14   9.80
to:TO 20.50 20.00   0.00 20.00 20.50
stop:VB 15.50 10.00 20.00   7.66 15.50
its:PRP$ 12.50 13.00 20.00 15.00 12.50
cooperation:NN 10.50 15.00 20.00 15.00   9.50
inspectors:NNS 10.50 15.00 20.00 15.00   9.88
NO_WORD 10.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.98 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : stay
-0.10  1.00 NullPunisher.functionWord : to
-2.00  1.00 RootEntailment.unalignedRoot : "is" not aligned to anything
Hand-tuned score (dot product of above): -1.4757
Threshold: -1.8794


Inference ID: 1219

Txt: The Tunisian Foreign Ministry announced that Libyan leader Colonel Moammar Al Kaddafy arrived in Tunis this morning, Monday, for a "brotherly and recreational" visit, at the invitation of Tunisian President Zine El Abidine Bin Ali.

Hyp: Libya's leader Colonel Muammar Qaddafi arrived in Tunisia at the invitation of President Zine El Abidine Bin Ali. (yes)

Libya
NNP
leader
NN
Colonel
NNP
Muammar_Qaddafi
NNP
arrived
VBD
Tunisia
NNP
the
DT
invitation
NN
President
NNP
Zine_El_Abidine_Bin_Ali
NNP
The:DT 20.50 20.00 20.00 20.50 20.00 20.50   0.00 20.00 20.00 20.50
Tunisian_Foreign_Ministry:NNP 10.50 10.50 10.50 10.50 15.50   5.50 20.50 10.50 10.50 10.50
announced:VBD 15.50 13.50 15.00 15.50   6.31 15.50 20.00 11.79 15.00 15.50
that:IN 20.50 20.00 20.00 20.50 20.00 20.50 20.00 20.00 20.00 20.50
Libyan:NNP   3.00   8.00   9.20 10.50 15.50 10.00 20.50   9.65   8.73 10.15
leader:NN   8.51   0.00   7.62 10.50 14.40 10.50 20.00   6.87   1.78   8.30
Colonel:NNP   9.92   7.62   0.00 10.50 15.00 10.50 20.00   9.16   8.26 10.19
Moammar_Al_Kaddafy:NNP   9.84   7.71   9.86 10.00 15.50 10.50 20.50   9.38   8.50   9.29
arrived:VBD 15.50 14.40 15.00 15.50   0.00 15.50 20.00 12.04 15.00 15.50
Tunis:NNP 10.00 10.50 10.50 10.50 15.50   5.00 20.50 10.50 10.50 10.50
this:DT 20.50 20.50 20.50 20.50 20.50 20.50 10.50 20.50 20.50 20.50
morning:NN   9.97   7.89 10.05 10.50 11.15 10.50 20.50   7.95   8.66   9.15
Monday:NNP   9.79   7.44   9.89 10.50 15.50 10.50 20.50   8.65   8.29   8.84
a:DT 20.50 20.00 20.00 20.50 20.00 20.50 10.00 20.00 20.00 20.50
brotherly:JJ 12.50 12.00 12.00 12.50 10.87 12.50 20.00 12.00 12.00 12.50
recreational:JJ 12.50 12.00 12.00 12.50 12.00 12.50 20.00 11.54 12.00 12.50
visit:NN   9.87   7.13   9.46 10.50   9.98 10.50 20.00   4.41   7.95   9.48
the:DT 20.50 20.00 20.00 20.50 20.00 20.50   0.00 20.00 20.00 20.50
invitation:NN   9.82   6.87   9.16 10.50 12.04 10.50 20.00   0.00   7.73   8.84
Tunisian:NNP 10.50 10.00 10.00 10.50 15.00   3.17 20.00 10.00 10.00 10.50
President:NNP   9.15   1.78   8.26 10.50 15.00 10.50 20.00   7.73   0.00   8.98
Zine_El_Abidine_Bin_Ali:NNP 10.12   8.30 10.19 10.00 15.50 10.50 20.50   8.84   8.98   0.00
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.89 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  10.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Tunisian" of "Zine_El_Abidine_Bin_Ali" dropped on aligned hyp word "Zine_El_Abidine_Bin_Ali"
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: announced-VBD
-2.00  1.00 Person.mismatch : person mimatch between Muammar_Qaddafi and Moammar_Al_Kaddafy
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.3990
Threshold: -1.8794


Inference ID: 1172

Txt: The American House of Representatives is due to vote today, on a resolution submitted by the Chairman of the Judicial Committee, Henry Hyde, asking Congress to launch an investigation over charges brought forward by Independent Prosecutor-General Kenneth Star against President Clinton in the Monica Lewinsky affair.

Hyp: Voting will take place in the American House of Representatives today, on the President Clinton-Monica Lewinsky scandal. (yes)

Voting
NNP
will
MD
take
VB
place
NN
the
DT
American_House_of_Representatives
NNP
today
NN
the
DT
President
NNP
Clinton-Monica_Lewinsky
NNP
scandal
NN
The:DT 20.00 10.00 20.00 20.00   0.00 20.50 20.00   0.00 20.00 20.50 20.00
American_House_of_Representatives:NNP   8.46 20.50 15.50   7.28 20.50   0.00   8.36 20.50   6.46   8.58   8.90
is:VBZ 15.00 20.00 10.00 15.00 20.00 15.50 15.00 20.00 15.00 15.50 15.00
due:JJ 12.00 20.00 10.71 11.22 20.00 12.50 12.00 20.00 12.00 12.50 11.79
to:TO 20.00 10.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.50 20.00
vote:VB   6.00 20.00   8.23 13.28 20.00 15.50 13.67 20.00 15.00 15.50 15.00
today:NN   8.29 20.00 14.74   7.73 20.00   8.36   0.00 20.00   7.74   9.65   8.68
a:DT 20.00 10.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.50 20.00
resolution:NN   8.75 20.00 13.59   8.28 20.00   8.89   8.09 20.00   8.28   9.94   9.07
submitted:VBN 15.00 20.00   9.61 15.00 20.00 15.50 13.16 20.00 15.00 15.50 14.05
the:DT 20.00 10.00 20.00 20.00   0.00 20.50 20.00   0.00 20.00 20.50 20.00
Chairman:NNP   7.68 20.00 15.00   6.41 20.00   6.23   7.58 20.00   3.05   8.31   8.16
the:DT 20.00 10.00 20.00 20.00   0.00 20.50 20.00   0.00 20.00 20.50 20.00
Judicial_Committee:NNP   8.30 20.50 15.50   7.65 20.50   7.30   8.19 20.50   7.66   9.32   8.76
Henry_Hyde:NNP   9.18 20.50 15.50   8.70 20.50   8.81   8.50 20.50   8.71   9.40   9.52
asking:NNP   6.57 20.00 13.15   7.02 20.00   7.67   7.57 20.00   7.02   9.24   4.67
Congress:NNP   8.79 20.50 15.50   8.23 20.50   7.86   8.70 20.50   8.24   9.65   9.18
to:TO 20.00 10.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.50 20.00
launch:VB 15.00 20.00   7.69 13.74 20.00 15.50 15.00 20.00 15.00 15.50 14.47
an:DT 20.00 10.00 20.00 20.00 10.00 20.50 20.00 10.00 20.00 20.50 20.00
investigation:NN   8.58 20.00 15.00   8.08 20.00   8.70   8.50 20.00   8.09   9.84   4.74
charges:NNS   8.04 20.00 15.00   7.44 20.00   8.07   7.19 20.00   7.44   9.48   6.86
brought_forward:VBN 15.00 20.00 10.00 15.00 20.00 15.50 15.00 20.00 15.00 15.50 15.00
Independent:NNP   9.08 20.00 15.00   8.30 20.00   8.36   9.02 20.00   7.74   9.03   9.34
Prosecutor-General:NNP   8.71 20.00 15.00   7.78 20.00   7.76   8.63 20.00   7.12   8.91   9.04
Kenneth_Star:NNP   8.85 20.50 15.50   7.79 20.50   7.78   8.76 20.50   7.64   8.80   9.23
President_Clinton:NNP   9.21 20.00 15.00   8.49 20.00   8.58   9.15 20.00   5.00   0.50   9.45
the:DT 20.00 10.00 20.00 20.00   0.00 20.50 20.00   0.00 20.00 20.50 20.00
Monica_Lewinsky:NNP 10.50 20.50 15.50 10.50 20.50 10.50 10.50 20.50 10.50   0.00 10.50
affair:NN   8.36 20.00 15.00   7.82 20.00   8.44   8.27 20.00   7.82   9.70   4.56
NO_WORD 10.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.93 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  0.36 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "resolution" of "vote" dropped on aligned hyp word "Voting"
-1.00  1.00 NullPunisher.other : place
-0.05  1.00 NullPunisher.aux : will
-1.00  1.00 NullPunisher.other : take
-2.00  1.00 RootEntailment.unalignedRoot : "take" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.8113
Threshold: -1.8794


Inference ID: 1177

Txt: Nevertheless, 58 percent of Americans expressed their satisfaction with the President's job performance. 44 percent supported Nixon at the very beginning of the investigation into Watergate.

Hyp: 58% of Americans are unsatisfied with Clinton's job performance. (don't know)

58
CD
%
NN
Americans
NNPS
are
VBP
unsatisfied
VBN
Clinton
NNP
job
NN
performance
NN
Nevertheless:RB 20.50 15.50 15.00 20.00 20.00 15.50 15.00 15.00
58:CD   0.00 20.00 20.50 20.50 20.50 20.50 19.98 20.50
percent:NN 20.00   0.00   8.61 15.50 15.50   9.79   7.79   7.48
Americans:NNPS 20.50 10.50   0.00 15.00 15.00   8.58   6.53   7.49
expressed:VBD 19.99 15.50 15.00 10.00   8.09 15.50 15.00 14.38
their:PRP$ 20.50 12.50 12.00 15.00 15.00 12.50 12.00 12.00
satisfaction:NN 20.50   8.23   8.58 15.00 13.16 10.04   5.34   7.11
the:DT 20.50 20.50 20.00 20.00 20.00 20.50 20.00 20.00
President:NNP 20.50 10.50   5.96 15.00 15.00   7.65   6.37   7.36
job:NN 19.98 10.08   6.53 15.00 14.44   8.83   0.00   6.53
performance:NN 20.50   9.49   7.49 15.00 14.49   9.43   6.53   0.00
44:CD   0.98 20.00 20.50 20.50 20.50 20.50 20.49 20.50
percent:NN 20.00   0.00   8.61 15.50 15.50   9.79   7.79   7.48
supported:VBD 20.19 15.50 15.00 10.00   7.99 15.50 15.00 15.00
Nixon:NNP 20.50 10.50   8.24 15.50 15.50   5.20   8.52   9.19
the:DT 20.50 20.50 20.00 20.00 20.00 20.50 20.00 20.00
very:JJ 20.50 12.50 12.00 12.00 12.00 12.50 12.00 12.00
beginning:NN 20.50   9.40   7.13 15.00 13.17   9.21   6.09   5.10
the:DT 20.50 20.50 20.00 20.00 20.00 20.50 20.00 20.00
investigation:NN 20.50 10.24   8.20 15.00 15.00   9.84   7.40   8.20
Watergate:NNP 20.50 10.50   9.12 15.00 15.00 10.29   8.58   7.67
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.27 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Nevertheless" of "expressed" dropped on aligned hyp word "unsatisfied"
-0.05  1.00 NullPunisher.aux : are
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "unsatisfied" aligned badly to "expressed"
-1.00  1.00 Structure.relMismatch : text "percent" is nsubj of "expressed" while hyp "%" is nsubjpass of "unsatisfied" which aligned to text "expressed"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.4714
Threshold: -1.8794


Inference ID: 1179

Txt: Security authorities have declared a state of maximum emergency in Guatemala, which is located directly in the path of the hurricane.

Hyp: There is a state of maximum emergency in Guatemala because of the hurricane. (yes)

There
EX
is
VBZ
a
DT
state
NN
maximum
JJ
emergency
NN
Guatemala
NNP
the
DT
hurricane
NN
Security:NNP 20.00 15.00 20.00   7.31 12.00   8.12 10.50 20.00   8.13
authorities:NNS 20.00 15.00 20.00   1.40 11.59   6.17 10.50 20.00   7.75
have:VBP 20.00 10.00 20.00 15.00 12.00 15.00 15.50 20.00 15.00
declared:VBN 20.00 10.00 20.00 12.91 10.83 14.38 15.50 20.00 13.85
a:DT 10.00 20.00   0.00 20.00 20.00 20.00 20.50 10.00 20.00
state:NN 20.00 15.00 20.00   0.00 10.78   7.08 10.50 20.00   7.28
maximum:JJ 20.00 12.00 20.00 10.78   0.00 11.28 12.50 20.00 12.00
emergency:NN 20.00 15.00 20.00   7.08 11.28   0.00 10.50 20.00   6.29
Guatemala:NNP 20.50 15.50 20.50 10.50 12.50 10.50   0.00 20.50 10.50
which:WDT 10.00 20.00 10.00 20.00 20.00 20.00 20.50 10.00 20.00
is:VBZ 20.00   0.00 20.00 15.00 12.00 15.00 15.50 20.00 15.00
located:VBN 20.00 10.00 20.00 12.42 12.00 12.78 15.50 20.00 11.19
directly:RB 20.00 20.00 20.00 13.90 11.33 13.99 15.50 20.00 15.00
the:DT 10.00 20.00 10.00 20.00 20.00 20.00 20.50   0.00 20.00
path:NN 20.00 15.00 20.00   7.77 12.00   8.49 10.50 20.00   8.50
the:DT 10.00 20.00 10.00 20.00 20.00 20.00 20.50   0.00 20.00
hurricane:NN 20.00 15.00 20.00   7.28 12.00   6.29 10.50 20.00   0.00
NO_WORD   1.00 10.00   1.00 10.00   9.00 10.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.51 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  8.00 Alignment.hypSpan
 0.10  0.56 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "located" of "Guatemala" dropped on aligned hyp word "Guatemala"
-0.10  1.00 NullPunisher.functionWord : There
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "state" <-dobj-- "declared" vs. hyp "state" <-nsubj-- "is", which aligned to text "is"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.0887
Threshold: -1.8794


Inference ID: 1230

Txt: The Yemeni Islamic Jihad group emerged in 1992, and it is comprised of old warriors from Afghanistan.

Hyp: Yemen's Islamic Jihad group was set up by old Afgan warriors in 1992. (yes)

Yemen
NNP
Islamic_Jihad
NN
group
NN
was
VBD
set_up
VBN
old
JJ
Afgan
NNP
warriors
NNS
1992
CD
The:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.50
Yemeni:NNP   3.50 10.00 10.00 15.00 15.00 12.00 10.50 10.00 20.50
Islamic_Jihad:NN 10.50   0.00 10.00 15.00 15.00 12.00 10.50 10.00 20.50
group:NN 10.50 10.00   0.00 15.00 15.00 12.00 10.50   6.96 19.66
emerged:VBD 15.50 15.00 13.56 10.00 10.00   9.45 15.50 15.00 17.45
1992:CD 20.50 20.50 19.66 20.50 20.50 20.50 20.50 20.50   0.00
it:PRP 12.50 12.00 12.00 15.00 15.00 15.00 12.50 12.00 20.50
is:VBZ 15.50 15.00 15.00   0.00 10.00 12.00 15.50 15.00 20.50
comprised:VBN 15.50 15.00 10.24 10.00 10.00 12.00 15.50 15.00 20.50
old:JJ 12.50 12.00 12.00 12.00 12.00   0.00 12.50 11.75 20.50
warriors:NNS 10.50 10.00   6.96 15.00 15.00 11.75 10.50   0.00 20.50
Afghanistan:NNP   8.53 10.50   7.94 15.50 15.50 12.50 10.00   9.75 20.50
NO_WORD 10.00 10.00 10.00   1.00 10.00   9.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.44 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Afgan[Afgan-NNP]
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1992
-1.00  1.00 NullPunisher.other : set_up
-0.05  1.00 NullPunisher.aux : was
-3.00  1.00 NullPunisher.entity : Afgan
-2.00  1.00 RootEntailment.unalignedRoot : "set_up" not aligned to anything
Hand-tuned score (dot product of above): -4.1589
Threshold: -1.8794


Inference ID: 2100

Txt: Greenspan's stance has underpinned predictions the Fed will raise rates steadily this year.

Hyp: The position of Greenspan has propped up the predictions that the Fed will gradually elevate the east interest rates year. (don't know)

The
DT
position
NN
Greenspan
NNP
has
VBZ
propped_up
VBN
the
DT
predictions
NNS
that
IN
the
DT
Fed
NNP
will
MD
gradually
RB
elevate
VB
the
DT
east
JJ
interest_rates
NNS
year
NN
Greenspan:NNP 20.50 10.50   0.00 15.50 15.50 20.50 10.50 20.50 20.50   9.23 20.50 15.50 15.50 20.50 12.50 10.50 10.50
stance:NN 20.00   7.15 10.50 15.00 15.00 20.00   7.87 20.00 20.00   8.92 20.00 14.56 11.59 20.00 10.63   8.80   7.14
has:VBZ 20.00 15.00 15.50   0.00 10.00 20.00 15.00 20.00 20.00 15.50 20.00 20.00 10.00 20.00 12.00 15.00 15.00
underpinned:VBN 20.00 15.00 15.50 10.00 10.00 20.00 13.68 20.00 20.00 15.50 20.00 19.17   9.05 20.00 12.00 15.00 15.00
predictions:NNS 20.00   8.20 10.50 15.00 15.00 20.00   0.00 20.00 20.00   9.00 20.00 13.53 13.85 20.00 10.91   8.86   7.99
the:DT   0.00 20.00 20.50 20.00 20.00   0.00 20.00 20.00   0.00 20.50 10.00 20.00 20.00   0.00 20.00 20.00 20.00
Fed:NNP 20.50   7.51   9.23 15.50 15.50 20.50   9.00 20.50 20.50   0.00 20.50 15.50 15.50 20.50 12.50   8.87   7.81
will:MD 10.00 20.00 20.50 20.00 20.00 10.00 20.00 20.00 10.00 20.50   0.00 20.00 20.00 10.00 20.00 20.00 20.00
raise:VB 20.00 15.00 15.50 10.00 10.00 20.00 15.00 20.00 20.00 15.50 20.00 19.06   8.54 20.00 12.00 15.00 13.63
rates:NNS 20.00   7.55 10.50 15.00 15.00 20.00   8.49 20.00 20.00   8.41 20.00 12.65 14.77 20.00 12.00   5.00   7.30
steadily:RB 20.00 14.95 15.50 20.00 20.00 20.00 11.21 20.00 20.00 15.50 20.00   4.51 20.00 20.00 11.36 15.00 13.64
this:DT 10.00 20.00 20.50 20.00 20.00 10.00 20.00 18.04 10.00 20.50 10.00 20.00 20.00 10.00 20.00 20.00 20.00
year:NN 20.00   6.89 10.50 15.00 15.00 20.00   7.99 20.00 20.00   7.81 20.00 13.71 15.00 20.00 11.47   7.84   0.00
NO_WORD   1.00 10.00 10.00   1.00 10.00   1.00 10.00   1.00   1.00 10.00 10.00   9.00 10.00   1.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.92 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  7.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.53 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added east[east-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "steadily" of "raise" dropped on aligned hyp word "elevate"
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: rate -> interest_rates
-0.10  1.00 NullPunisher.functionWord : that
-1.00  1.00 NullPunisher.other : gradually
-0.10  1.00 NullPunisher.article : The
-0.05  1.00 NullPunisher.aux : will
-1.00  1.00 NullPunisher.other : east
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "propped_up" aligned badly to "underpinned"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "raise" <-rcmod-- "predictions" vs. hyp "elevate" <-ccomp-- "propped_up", which aligned to text "underpinned"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -8.9040
Threshold: -1.8794


Inference ID: 1215

Txt: A statement issued by Royal Moroccan Airlines, a copy of which was received by Agence France Presse today, said that the company decided to start two flights a week between Casablanca and Gaza as soon as the Palestinian airport is opened there.

Hyp: With the opening of the Palestinian airport, Royal Moroccan Airlines will fly between Casablanca and Gaza. (yes)

the
DT
opening
NN
the
DT
Palestinian
JJ
airport
NN
Royal_Moroccan_Airlines
NNP
will
MD
fly
VB
Casablanca
NNP
Gaza
NNP
A:DT 10.00 20.00 10.00 20.00 20.00 20.50 10.00 20.00 20.50 20.50
statement:NN 20.00   5.74 20.00 12.00   7.45   8.29 20.00 15.00   8.99   9.36
issued:VBN 20.00 14.29 20.00 12.00 14.28 15.50 20.00 10.00 15.50 15.50
Royal_Moroccan_Airlines:NNP 20.50   8.94 20.50 12.50   8.77   0.00 20.50 15.50   9.86   9.90
a:DT 10.00 20.00 10.00 20.00 20.00 20.50 10.00 20.00 20.50 20.50
copy:NN 20.00   8.09 20.00 12.00   9.08   9.77 20.00 14.32 10.10 10.26
of:IN 18.70 20.00 18.70 20.00 20.00 20.50 20.00 20.00 20.50 20.50
which:WDT 10.00 20.00 10.00 20.00 20.00 20.50 10.00 20.00 20.50 20.50
was:VBD 20.00 15.00 20.00 12.00 15.00 15.50 20.00 10.00 15.50 15.50
received:VBN 20.00 15.00 20.00 12.00 15.00 15.50 20.00 10.00 15.50 15.50
Agence_France_Presse:NNP 20.50   7.35 20.50 12.50   9.07   9.31 20.50 15.50   7.36   8.01
today:NN 20.50   7.90 20.50 12.50   9.13   9.36 20.50 15.50   9.81 10.04
said:VBD 20.00 15.00 20.00 12.00 15.00 15.50 20.00 10.00 15.50 15.50
that:IN 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50 20.50
the:DT   0.00 20.00   0.00 20.00 20.00 20.50 10.00 20.00 20.50 20.50
company:NN 20.00   7.12 20.00 12.00   7.80   8.61 20.00 14.28   9.24   9.57
decided:VBD 20.00 12.70 20.00 12.00 15.00 15.50 20.00   8.71 15.50 15.50
to:TO 10.00 20.00 10.00 20.00 20.00 20.50 10.00 20.00 20.50 20.50
start:VB 20.00 11.17 20.00 12.00 14.71 15.50 20.00   7.78 15.50 15.50
two:CD 20.50 18.32 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
flights:NNS 20.00   8.50 20.00 12.00   3.91   9.64 20.00   2.50 10.02 10.20
a:DT 10.00 20.00 10.00 20.00 20.00 20.50 10.00 20.00 20.50 20.50
week:NN 20.00   6.94 20.00 12.00   8.31   9.08 20.00 15.00   9.60   9.87
Casablanca:NNP 20.50   8.09 20.50 12.50   9.83   9.86 20.50 15.50   0.00   7.11
Gaza:NNP 20.50   8.26 20.50 12.50   9.87   9.90 20.50 15.50   7.11   0.00
as:RB 20.00 15.00 20.00 12.00 15.00 15.50 20.00 20.00 15.50 15.50
soon:RB 20.00 13.67 20.00 12.00 13.98 15.50 20.00 17.18 15.50 15.50
as:IN 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50 20.50
the:DT   0.00 20.00   0.00 20.00 20.00 20.50 10.00 20.00 20.50 20.50
Palestinian:JJ 20.00 12.00 20.00   0.00 12.00 12.50 20.00 12.00 12.50 12.50
airport:NN 20.00   8.16 20.00 12.00   0.00   8.77 20.00   9.50   9.83   9.87
is:VBZ 20.00 15.00 20.00 12.00 15.00 15.50 20.00 10.00 15.50 15.50
opened:VBN 20.00   6.00 20.00 12.00 13.93 15.50 20.00   9.00 15.50 15.50
there:RB 20.00 15.00 20.00 12.00 15.00 15.50 20.00 20.00 15.50 15.50
NO_WORD   1.00 10.00   1.00   9.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.44 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "there" of "opened" dropped on aligned hyp word "opening"
-1.00  1.00 NullPunisher.other : fly
-0.10  1.00 NullPunisher.article : the
-0.05  1.00 NullPunisher.aux : will
-2.00  1.00 RootEntailment.unalignedRoot : "fly" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.4535
Threshold: -1.8794


Inference ID: 1207

Txt: Eddeh reasserted his opinion that President Al-Hrawi ought to be reinstated for another 3 years, claiming that the he "has considerable experience in the running of the state, but his resignation would be appropriate after the withdrawal of Israeli and Syrian forces."

Hyp: Eddeh calls for an extension to Al-Hrawi's presidency, saying that he should resign after the withdrawal of the Israeli and Syrian forces." (yes)

Eddeh
NNP
calls
VBZ
an
DT
extension
NN
Al-Hrawi
NNP
presidency
NN
saying
VBG
that
IN
he
PRP
should
MD
resign
VB
the
DT
withdrawal
NN
the
DT
Israeli
JJ
Syrian
JJ
forces
NNS
Eddeh:NNP   0.00 15.50 20.50 10.50 10.50 10.50 15.50 20.50 12.50 20.50 15.50 20.50 10.50 20.50 12.50 12.50 10.50
reasserted:VBD 15.50   8.49 20.00 15.00 15.50 15.00   9.10 20.00 15.00 20.00 10.00 20.00 15.00 20.00 12.00 12.00 13.03
his:PRP$ 12.50 15.00 20.00 12.00 12.50 12.00 15.00 20.00 10.00 20.00 15.00 20.00 12.00 20.00 15.00 15.00 12.00
opinion:NN 10.50 15.00 20.00   8.69   9.04   6.77 13.33 20.00 12.00 20.00 14.32 20.00   8.58 20.00 12.00 12.00   6.80
that:IN 20.50 20.00 20.00 20.00 20.50 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
President:NNP 10.50 15.00 20.00   8.57   8.50   1.00 15.00 20.00 12.00 20.00 15.00 20.00   8.47 20.00 12.00 12.00   6.61
Al-Hrawi:NNP 10.00 15.50 20.50   9.84   0.50   9.31 15.50 20.50 12.50 20.50 15.50 20.50   9.78 20.50 12.50 12.50   8.49
ought:MD 20.50 20.00 10.00 20.00 20.50 17.58 17.70 20.00 20.00 10.00 20.00 10.00 20.00 10.00 20.00 20.00 20.00
to:TO 20.50 20.00   8.20 20.00 20.50 20.00 20.00 20.00 20.00 10.00 20.00 10.00 20.00 10.00 20.00 20.00 20.00
be:VB 15.50 10.00 20.00 15.00 15.50 15.00 10.00 20.00 15.00 20.00 10.00 20.00 15.00 20.00 12.00 12.00 15.00
reinstated:VBN 15.50   9.26 20.00 14.88 15.50 15.00   9.03 20.00 15.00 20.00 10.00 20.00 14.69 20.00 12.00 12.00 14.63
another:DT 20.50 20.00 10.00 20.00 20.50 20.00 20.00 20.00 20.00 10.00 20.00 10.00 20.00 10.00 20.00 20.00 20.00
3:CD 20.50 19.99 20.50 20.50 20.50 20.50 20.49 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
years:NNS 10.50 15.00 20.00   7.10   8.90   5.00 14.88 20.00 12.00 20.00 15.00 20.00   8.45 20.00 12.00 12.00   6.57
claiming:VBG 15.50   8.70 20.00 14.17 15.50 13.79   7.19 20.00 15.00 20.00   9.45 20.00 15.00 20.00 12.00 12.00 14.89
that:IN 20.50 20.00 20.00 20.00 20.50 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.50 18.69 10.00 20.00 20.50 20.00 20.00 20.00 18.00 10.00 20.00   0.00 20.00   0.00 20.00 20.00 20.00
he:PRP 12.50 15.00 20.00 12.00 12.50 12.00 15.00 20.00   0.00 20.00 15.00 18.00 12.00 18.00 15.00 15.00 12.00
has:VBZ 15.50 10.00 20.00 15.00 15.50 15.00 10.00 20.00 15.00 20.00 10.00 20.00 15.00 20.00 12.00 12.00 15.00
considerable:JJ 12.50 12.00 20.00 10.66 12.50 10.34 12.00 20.00 15.00 20.00 11.83 20.00   8.48 20.00 10.00 10.00   9.44
experience:NN 10.50 15.00 20.00   9.28   9.69   8.72 15.00 20.00 12.00 20.00 15.00 20.00   9.21 20.00 12.00 12.00   7.87
the:DT 20.50 18.69 10.00 20.00 20.50 20.00 20.00 20.00 18.00 10.00 20.00   0.00 20.00   0.00 20.00 20.00 20.00
running:NN 10.50 14.95 20.00   8.92   9.30   7.01 14.05 20.00 12.00 20.00 14.04 20.00   8.83 20.00 12.00 12.00   7.21
the:DT 20.50 18.69 10.00 20.00 20.50 20.00 20.00 20.00 18.00 10.00 20.00   0.00 20.00   0.00 20.00 20.00 20.00
state:NN 10.50 13.91 20.00   8.68   9.04   7.90 14.93 20.00 12.00 20.00 15.00 20.00   8.58 20.00 12.00 12.00   4.53
his:PRP$ 12.50 15.00 20.00 12.00 12.50 12.00 15.00 20.00 10.00 20.00 15.00 20.00 12.00 20.00 15.00 15.00 12.00
resignation:NN 10.50 14.41 20.00   9.26   9.69   7.40 12.24 20.00 12.00 20.00   1.00 20.00   8.64 20.00 12.00 12.00   7.88
would:MD 20.50 20.00 10.00 20.00 20.50 20.00 20.00 20.00 20.00   5.00 20.00 10.00 20.00 10.00 20.00 20.00 20.00
be:VB 15.50 10.00 20.00 15.00 15.50 15.00 10.00 20.00 15.00 20.00 10.00 20.00 15.00 20.00 12.00 12.00 15.00
appropriate:JJ 12.50 11.53 20.00 10.63 12.50 12.00 10.48 20.00 15.00 20.00 11.09 20.00 10.59 20.00 10.00 10.00 12.00
the:DT 20.50 18.69 10.00 20.00 20.50 20.00 20.00 20.00 18.00 10.00 20.00   0.00 20.00   0.00 20.00 20.00 20.00
withdrawal:NN 10.50 15.00 20.00   7.55   9.78   6.83 15.00 20.00 12.00 20.00 13.79 20.00   0.00 20.00 12.00 12.00   4.60
Israeli:JJ 12.50 12.00 20.00 12.00 12.50 12.00 12.00 20.00 15.00 20.00 12.00 20.00 12.00 20.00   0.00   6.82 12.00
Syrian:JJ 12.50 12.00 20.00 12.00 12.50 12.00 12.00 20.00 15.00 20.00 12.00 20.00 12.00 20.00   6.82   0.00 12.00
forces:NNS 10.50 15.00 20.00   8.16   8.49   7.23 15.00 20.00 12.00 20.00 15.00 20.00   4.60 20.00 12.00 12.00   0.00
NO_WORD 10.00 10.00   1.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00   1.00 10.00   1.00   9.00   9.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.15 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added saying[saying-VBG]
-0.10  1.00 NullPunisher.article : an
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : saying
-1.00  1.00 NullPunisher.other : extension
-0.05  1.00 NullPunisher.aux : should
-1.00  1.00 NullPunisher.other : calls
-2.00  1.00 RootEntailment.unalignedRoot : "calls" not aligned to anything
Hand-tuned score (dot product of above): -4.4703
Threshold: -1.8794


Inference ID: 2101

Txt: Both candidates are making a major push in Iowa.

Hyp: Both candidates are delivering a great attack in Iowa. (don't know)

Both
DT
candidates
NNS
are
VBP
delivering
VBG
a
DT
great
JJ
attack
NN
Iowa
NNP
Both:DT   0.00 20.00 20.00 20.00 10.00 20.00 20.00 20.50
candidates:NNS 20.00   0.00 15.00 14.20 20.00 12.00   8.42   8.83
are:VBP 20.00 15.00   0.00 10.00 20.00 12.00 15.00 15.50
making:VBG 20.00 14.63 10.00   7.88 20.00   8.67 14.74 15.50
a:DT 10.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
major:JJ 20.00 10.91 12.00 11.51 20.00   8.46 11.03 12.50
push:NN 20.00   8.54 15.00 12.82 20.00 12.00   6.85   9.42
Iowa:NNP 20.50   8.83 15.50 15.50 20.50 12.50   9.32   0.00
NO_WORD 10.00 10.00   1.00 10.00   1.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.03 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  8.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "delivering" aligned badly to "making"
Hand-tuned score (dot product of above): 1.8077
Threshold: -1.8794


Inference ID: 2111

Txt: Slavkov, accused in the Salt Lake scandal but later cleared, said he had been approached by Takatch.

Hyp: Slavkov, defendant in the scandal of Salt Lake but that later was exculpated, in fact said that Takatch. (don't know)

Slavkov
NNP
defendant
NN
the
DT
scandal
NN
Salt_Lake
NNP
but
CC
that
WDT
later
RB
was
VBD
exculpated
VBN
fact
NN
said
VBD
that
DT
Takatch
NNP
Slavkov:NNP   0.00 10.50 20.50 10.50 10.50 20.50 20.50 15.50 15.50 15.50 10.50 15.50 20.50 10.00
accused:VBN 15.50 11.75 20.00   8.91 15.50 20.00 20.00 18.48 10.00 10.00 13.97   8.33 20.00 15.50
the:DT 20.50 20.00   0.00 20.00 20.50 10.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.50
Salt_Lake:NNP 10.50   9.26 20.50   9.67   0.00 20.50 20.50 15.50 15.50 15.50   8.80 15.50 20.50 10.50
scandal:NN 10.50   8.98 20.00   0.00   9.67 20.00 20.00 13.14 15.00 15.00   8.08 14.82 20.00 10.50
later:RB 15.50 14.81 20.00 13.14 15.50 20.00 20.00   0.00 20.00 20.00 15.00 19.70 20.00 15.50
cleared:VBN 15.50 12.61 20.00 13.71 15.50 20.00 20.00 17.66 10.00 10.00 15.00   9.51 20.00 15.50
said:VBD 15.50 15.00 20.00 14.82 15.50 20.00 20.00 19.70 10.00 10.00 14.21   0.00 20.00 15.50
he:PRP 12.50 12.00 18.00 12.00 12.50 20.00 20.00 20.00 15.00 15.00 12.00 15.00 20.00 12.50
had:VBD 15.50 15.00 20.00 15.00 15.50 20.00 20.00 20.00 10.00 10.00 15.00 10.00 20.00 15.50
been:VBN 15.50 15.00 20.00 15.00 15.50 20.00 20.00 20.00   0.00 10.00 15.00 10.00 20.00 15.50
approached:VBN 15.50 15.00 20.00 13.02 15.50 20.00 20.00 16.64 10.00 10.00 15.00   8.58 20.00 15.50
Takatch:NNP 10.00 10.50 20.50 10.50 10.50 20.50 20.50 15.50 15.50 15.50 10.50 15.50 20.50   0.00
NO_WORD 10.00 10.00   1.00 10.00 10.00 10.00 10.00   9.00   1.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.94 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  7.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added fact[fact-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "accused" of "Slavkov" dropped on aligned hyp word "Slavkov"
-1.00  1.00 NullPunisher.other : exculpated
-1.00  1.00 NullPunisher.other : but
-1.00  1.00 NullPunisher.other : fact
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : defendant
-1.00  1.00 NullPunisher.other : that
-0.10  1.00 NullPunisher.functionWord : that
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.7043
Threshold: -1.8794


Inference ID: 1150

Txt: The humane organization, Caritas, quoting its branch in Iraq, announced today, in Germany, that several Iraqi hospitals, especially Saddam Hospital, which is considered the biggest hospital in Iraq, were hit in the American-British bombing of the Iraqi capital.

Hyp: Caritas announced that several Iraqi hospitals, (excluding Saddam Hospital,) were hit in the shelling of Baghdad. (don't know)

Caritas
NNP
announced
VBD
that
IN
several
JJ
Iraqi
NNP
hospitals
NNS
-LRB-
-LRB-
Saddam_Hospital
NNP
were
VBD
hit
VBN
the
DT
shelling
NN
Baghdad
NNP
The:DT 20.50 20.00 20.00 20.00 20.50 20.00 10.00 20.50 20.00 20.00   0.00 20.00 20.50
humane:JJ 12.50 12.00 20.00 10.00 12.50   9.64 20.00 12.50 12.00 12.00 20.00 10.41 12.50
organization:NN 10.50 15.00 20.00 12.00 10.50   6.38 20.00 10.50 15.00 15.00 20.00   7.42   9.32
Caritas:NNP   0.00 15.50 20.50 12.50 10.00 10.50 20.50 10.50 15.50 15.50 20.50 10.50 10.00
quoting:VBG 15.50   9.78 20.00 12.00 15.50 15.00 20.00 15.50 10.00 10.00 20.00 13.12 15.50
its:PRP$ 12.50 15.00 20.00 15.00 12.50 12.00 20.00 12.50 15.00 15.00 20.00 12.00 12.50
branch:NN 10.50 14.93 20.00 12.00 10.50   8.63 20.00 10.50 15.00 15.00 20.00   9.18 10.29
Iraq:NNP 10.00 15.50 20.50 12.50   3.00   9.57 20.50 10.50 15.50 15.50 20.50 10.17   6.98
announced:VBD 15.50   0.00 20.00 12.00 15.50 14.79 20.00 15.50 10.00 10.00 20.00 13.93 15.50
today:NN 10.50 13.38 20.50 12.50 10.50   8.79 20.50 10.50 15.50 15.50 20.50   9.43 10.18
Germany:NNP 10.00 15.50 20.50 12.50 10.00   8.61 20.50 10.50 15.50 15.50 20.50   9.60   7.18
that:IN 20.50 20.00   0.00 20.00 20.50 20.00 20.00 20.50 20.00 20.00 20.00 20.00 20.50
several:JJ 12.50 12.00 20.00   0.00 12.50 12.00 20.00 12.50 12.00 12.00 20.00 12.00 12.50
Iraqi:NNP 10.00 15.50 20.50 12.50   0.00 10.50 20.50 10.50 15.50 15.50 20.50 10.50   9.04
hospitals:NNS 10.50 14.79 20.00 12.00 10.50   0.00 20.00   5.50 15.00 15.00 20.00   9.00   9.87
especially:RB 15.50 20.00 20.00 12.00 15.50 14.80 20.00 15.50 20.00 19.19 20.00 14.38 15.50
Saddam_Hospital:NNP 10.50 15.50 20.50 12.50 10.50   5.50 20.50   0.00 15.50 15.50 20.50 10.50 10.50
which:WDT 20.50 20.00 20.00 20.00 20.50 20.00 10.00 20.50 20.00 20.00 10.00 20.00 20.50
is:VBZ 15.50 10.00 20.00 12.00 15.50 15.00 20.00 15.50   0.00 10.00 20.00 15.00 15.50
considered:VBN 15.50 10.00 20.00 12.00 15.50 15.00 20.00 15.50 10.00 10.00 20.00 15.00 15.50
the:DT 20.50 20.00 20.00 20.00 20.50 20.00 10.00 20.50 20.00 20.00   0.00 20.00 20.50
biggest:JJS 12.50 10.10 20.00 10.00 12.50 12.00 20.00 12.50 12.00 11.46 20.00 12.00 12.50
hospital:NN 10.50 14.01 20.00 12.00 10.50   0.00 20.00   5.50 15.00 14.52 20.00   9.00   9.87
Iraq:NNP 10.00 15.50 20.50 12.50   3.00   9.57 20.50 10.50 15.50 15.50 20.50 10.17   6.98
were:VBD 15.50 10.00 20.00 12.00 15.50 15.00 20.00 15.50   0.00 10.00 20.00 15.00 15.50
hit:VBN 15.50 10.00 20.00 12.00 15.50 15.00 20.00 15.50 10.00   0.00 20.00 14.71 15.50
the:DT 20.50 20.00 20.00 20.00 20.50 20.00 10.00 20.50 20.00 20.00   0.00 20.00 20.50
American-British:JJ 12.50 12.00 20.00 10.00 12.50 12.00 20.00 12.50 12.00 12.00 20.00 12.00 12.50
bombing:NN 10.50 15.00 20.00 12.00 10.50   9.35 20.00 10.50 15.00 14.25 20.00   4.01 10.47
the:DT 20.50 20.00 20.00 20.00 20.50 20.00 10.00 20.50 20.00 20.00   0.00 20.00 20.50
Iraqi:NNP 10.00 15.50 20.50 12.50   0.00 10.50 20.50 10.50 15.50 15.50 20.50 10.50   9.04
capital:NN 10.50 15.00 20.00 12.00 10.50   5.00 20.00 10.50 15.00 15.00 20.00   8.09   9.21
NO_WORD 10.00 10.00   1.00   9.00 10.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.40 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.31 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "bombing" of "hit" dropped on aligned hyp word "hit"
-1.00  1.00 NullPunisher.other : -LRB-
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.8462
Threshold: -1.8794


Inference ID: 2094

Txt: It was like hitting the jackpot.

Hyp: It was like removing the lottery. (don't know)

It
PRP
was
VBD
like
IN
removing
VBG
the
DT
lottery
NN
It:PRP   0.00 15.00 20.00 15.00 20.00 12.00
was:VBD 15.00   0.00 20.00 10.00 20.00 15.00
like:IN 20.00 20.00   0.00 20.00 20.00 19.28
hitting_the_jackpot:VBG 15.00 10.00 20.00 10.00 20.00 15.00
NO_WORD 10.00 10.00 10.00 10.00   1.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.77 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : like
-1.00  1.00 NullPunisher.other : removing
-1.00  1.00 NullPunisher.other : lottery
Hand-tuned score (dot product of above): -1.6453
Threshold: -1.8794


Inference ID: 1205

Txt: Israel controls an area of about 850 square kilometres in the south of the Lebanese Republic, while Syria has about 30 thousand soldiers over two thirds of the Lebanese territory.

Hyp: Syria has 30,000 soldiers in 60% of Lebanon, and Israel controls an area in the south of 850 square kms. (yes)

Syria
NNP
has
VBZ
30,000
CD
soldiers
NNS
60
CD
%
NN
Lebanon
NNP
Israel
NNP
controls
VBZ
an
DT
area
NN
the
DT
south
NN
850
CD
square
JJ
kms
NN
Israel:NNP   6.21 15.50 20.50   8.90 20.50 10.50   5.79   0.00 15.50 20.50   6.22 20.50   7.36 20.50 12.50 10.16
controls:VBZ 15.50 10.00 20.50 14.48 18.35 15.50 15.50 15.50   0.00 20.00 14.93 20.00 13.96 20.50 10.37 15.00
an:DT 20.50 20.00 20.50 20.00 20.50 20.50 20.50 20.50 20.00   0.00 20.00 10.00 20.00 20.50 20.00 20.00
area:NN   7.22 15.00 19.39   6.58 20.50 10.25   6.53   6.22 14.93 20.00   0.00 20.00   4.74 20.12   7.00   8.80
about:RB 15.50 20.00 20.50 15.00 20.50 15.50 15.50 15.50 20.00 18.13 15.00 18.39 15.00 20.50 12.00 15.00
850:CD 20.50 20.50   5.00 19.94 10.02 20.50 20.50 20.50 20.50 20.50 20.12 20.50 19.55   0.00 16.68 20.50
square:JJ 12.50 12.00 20.03 11.92 19.41 12.50 12.50 12.50 10.37 20.00   7.00 20.00   9.60 16.68   0.00 12.00
kilometres:NNS 10.39 15.00 20.50   9.35 20.50 10.50 10.23 10.16 15.00 20.00   8.80 20.00   9.47 20.50 12.00   0.00
the:DT 20.50 20.00 20.50 20.00 20.50 20.50 20.50 20.50 20.00 10.00 20.00   0.00 20.00 20.50 20.00 20.00
south:NN   7.79 15.00 19.88   7.93 20.23   9.95   7.47   7.36 13.96 20.00   4.74 20.00   0.00 19.55   9.60   9.47
the:DT 20.50 20.00 20.50 20.00 20.50 20.50 20.50 20.50 20.00 10.00 20.00   0.00 20.00 20.50 20.00 20.00
Lebanese_Republic:NNP   4.51 15.50 20.50   9.07 20.50 10.50   0.00   5.79 15.50 20.50   6.53 20.50   7.47 20.50 12.50 10.23
while:IN 20.50 20.00 20.50 20.00 20.50 20.50 20.50 20.50 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00
Syria:NNP   0.00 15.50 20.50   9.57 20.50 10.50   4.51   6.21 15.50 20.50   7.22 20.50   7.79 20.50 12.50 10.39
has:VBZ 15.50   0.00 20.50 15.00 20.50 15.50 15.50 15.50 10.00 20.00 15.00 20.00 15.00 20.50 12.00 15.00
about:RB 15.50 20.00 20.50 15.00 20.50 15.50 15.50 15.50 20.00 18.13 15.00 18.39 15.00 20.50 12.00 15.00
30:CD 20.50 20.50   5.00 20.50   3.04 19.44 20.50 20.50 20.50 20.50 20.41 20.50 20.50   5.00 19.11 20.50
thousand:CD 20.50 20.50   2.92 18.49   9.56 20.13 20.50 20.50 20.50 20.50 18.59 20.50 19.24   5.00 18.86 20.50
soldiers:NNS   9.57 15.00 18.50   0.00 20.50   9.97   9.07   8.90 14.48 20.00   6.58 20.00   7.93 19.94 11.92   9.35
two:CD 20.50 20.50   5.00 20.50   8.00 20.50 20.50 20.50 20.50 20.50 20.15 20.50 20.11   5.00 20.18 20.50
thirds:NNS 10.02 15.00 20.50   8.50 20.50 10.50   9.67   9.54 14.90 20.00   7.63 20.00   8.70 20.11 12.00   6.68
the:DT 20.50 20.00 20.50 20.00 20.50 20.50 20.50 20.50 20.00 10.00 20.00   0.00 20.00 20.50 20.00 20.00
Lebanese:JJ 12.50 12.00 20.50 12.00 20.50 12.50   5.50 12.50 12.00 20.00 12.00 20.00 12.00 20.50 10.00 12.00
territory:NN   6.47 15.00 20.50   5.10 20.03 10.24   5.45   5.08 12.24 20.00   1.66 20.00   4.08 20.50 10.41   8.44
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.22 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  16.00 Alignment.hypSpan
 0.10  0.38 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "about" of "850" dropped on aligned hyp word "850"
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: territory -> Lebanon
 1.00  1.00 Quant.contract : [an,an]
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 4.7560
Threshold: -1.8794


Inference ID: 1164

Txt: Ramadan told reporters at the opening ceremony of the Baghdad International Exposition, "No cooperation and no inspection or monitoring by the American Zionist espionage commission (the Special Commission for disarming Iraq's banned weapons - UNSCOM) before Iraq's demands are met."

Hyp: Ramadan announced that Iraq will not cooperate with the inspectors. (yes)

Ramadan
NNP
announced
VBD
that
IN
Iraq
NNP
will
MD
not
RB
cooperate
VB
the
DT
inspectors
NNS
Ramadan:NNP   0.50 15.50 20.50 10.50 20.50 15.50 15.50 20.50 10.50
told:VBD 15.50   7.27 20.00 15.50 20.00 20.00   7.66 20.00 13.82
reporters:NNS 10.50 12.68 20.00   9.81 20.00 15.00 12.18 20.00   8.38
the:DT 20.50 20.00 20.00 20.50 10.00 20.00 20.00   0.00 20.00
opening:VBG 15.50   7.64 20.00 15.50 20.00 20.00   9.05 20.00 15.00
ceremony:NN 10.50 10.81 20.00   9.94 20.00 15.00 14.98 20.00   9.25
the:DT 20.50 20.00 20.00 20.50 10.00 20.00 20.00   0.00 20.00
Baghdad:NNP 10.00 15.50 20.50   6.98 20.50 15.50 15.50 20.50   9.95
International:NNP 10.50 15.00 20.00   9.79 20.00 15.00 15.00 20.00   9.08
Exposition:NNP 10.50 15.00 20.00 10.43 20.00 15.00 15.00 20.00   9.87
No:DT 20.50 20.00 20.00 20.50 10.00 20.00 20.00 10.00 20.00
cooperation:NN 10.50 14.16 20.00   9.50 20.00 15.00   1.00 20.00   8.19
no:DT 20.50 20.00 20.00 20.50 10.00 18.00 20.00 10.00 20.00
inspection:NN 10.50 14.63 20.00   9.95 20.00 15.00 12.10 20.00   1.92
monitoring:VBG 15.50   9.67 20.00 15.50 20.00 20.00   5.65 20.00   9.51
the:DT 20.50 20.00 20.00 20.50 10.00 20.00 20.00   0.00 20.00
American:JJ 12.50 12.00 20.00 12.50 20.00 12.00 12.00 20.00 12.00
Zionist:NNP 10.50 15.00 20.00 10.50 20.00 15.00 15.00 20.00 10.00
espionage:NN 10.50 15.00 20.00 10.37 20.00 15.00 12.50 20.00   6.73
commission:NN 10.50 15.00 20.00   9.47 20.00 15.00 12.25 20.00   6.84
the:DT 20.50 20.00 20.00 20.50 10.00 20.00 20.00   0.00 20.00
Special_Commission:NNP 10.50 15.50 20.50 10.31 20.50 15.50 15.50 20.50 10.20
disarming:NNP 10.50 14.58 20.00 10.41 20.00 15.00 13.25 20.00   8.83
Iraq:NNP 10.00 15.50 20.50   0.00 20.50 15.50 15.50 20.50   9.88
's:VBZ 15.50   6.56 20.00 15.50 20.00 20.00   9.94 20.00 15.00
banned:VBN 15.50   9.70 20.00 15.50 20.00 20.00   8.30 20.00 13.80
weapons:NNS 10.50 15.00 20.00   9.29 20.00 15.00 12.44 20.00   6.94
UNSCOM:NNP 10.50 15.50 20.50   9.33 20.50 15.50 15.50 20.50 10.50
before:IN 20.50 20.00 10.00 20.50 20.00 20.00 20.00 20.00 20.00
Iraq:NNP 10.00 15.50 20.50   0.00 20.50 15.50 15.50 20.50   9.88
demands:NNS 10.50 13.29 20.00   9.71 20.00 15.00 12.27 20.00   7.63
are:VBP 15.50 10.00 20.00 15.50 20.00 20.00 10.00 20.00 15.00
met:VBN 15.50   5.96 20.00 15.50 20.00 20.00   7.84 20.00 15.00
NO_WORD 10.00 10.00   1.00 10.00 10.00   9.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.90 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.11 Alignment.txtSpan
 0.00  1.00 NegPolarity.hypNegWord : "cooperate": has child with relation "neg"
-1.00  1.00 NullPunisher.other : not
-1.00  1.00 NullPunisher.other : announced
-0.05  1.00 NullPunisher.aux : will
-0.10  1.00 NullPunisher.functionWord : that
-0.10  1.00 NullPunisher.article : the
-6.00  1.00 Quant.oneNo : [no,the[
-2.00  1.00 RootEntailment.unalignedRoot : "announced" not aligned to anything
Hand-tuned score (dot product of above): -8.8720
Threshold: -1.8794


Inference ID: 1246

Txt: The Iraqi Foreign Minister, Mohammad Said Al-Sahaf, met today with the new ambassadors and briefed them "on the Iraqi diplomacy directives of escalating the move toward lifting the blockade imposed on Iraq and developing relations with various countries of the world."

Hyp: Mohammed Said AL-SAHAF met with the ambassadors and briefed them on Iraq's diplomacy directives. (yes)

Mohammed_Said_AL-SAHAF
NNP
met
VBD
the
DT
ambassadors
NNS
briefed
VBD
ambassadors
NNS
Iraq
NNP
diplomacy
NN
directives
NNS
The:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.00
Iraqi:JJ 12.50 12.00 20.00 12.00 12.00 12.00   5.50 12.00 12.00
Foreign_Minister:NNP 10.50 15.00 20.00 10.00 15.00 10.00 10.50 10.00 10.00
Mohammad_Said_Al-Sahaf:NNP   0.50 15.50 20.50   9.24 15.50   9.24   9.97 10.37 10.46
met:VBD 15.50   0.00 20.00 11.36   3.32 11.36 15.50 12.17 14.72
today:NN 10.18 13.27 20.50   9.54 13.36   9.54   9.77   8.77   9.29
the:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.00
new:JJ 12.50 10.97 20.00 12.00 12.00 12.00 12.50 11.16 11.30
ambassadors:NNS   9.24 11.36 20.00   0.00 10.85   0.00   9.87   5.66   8.63
briefed:VBD 15.50   3.32 20.00 10.85   0.00 10.85 15.50 12.81 13.49
ambassadors:NNS   9.24 11.36 20.00   0.00 10.85   0.00   9.87   5.66   8.63
the:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.00
Iraqi:NNP 10.50 15.50 20.50 10.50 15.50 10.50   3.00 10.50 10.50
diplomacy:NN 10.37 12.17 20.00   5.66 12.81   5.66 10.11   0.00   7.61
directives:NNS 10.46 14.72 20.00   8.63 13.49   8.63 10.31   7.61   0.00
of:IN 20.50 20.00 18.70 20.00 20.00 20.00 20.50 20.00 20.00
escalating:VBG 15.50   9.50 20.00 15.00   8.65 15.00 15.50 13.34 14.86
the:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.00
move:NN   9.97 15.00 20.00   8.66 15.00   8.66   9.45   8.39   8.89
toward:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00
lifting:VBG 15.50   8.07 20.00 12.67   8.64 12.67 15.50   9.45 14.65
the:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.00
blockade:NN 10.34 14.65 20.00   5.43 13.35   5.43 10.04   5.73   7.89
imposed:VBN 15.50 10.00 20.00 14.07 10.00 14.07 15.50 12.89 11.68
Iraq:NNP   9.97 15.50 20.50   9.87 15.50   9.87   0.00 10.11 10.31
developing:VBG 15.50   9.65 20.00 14.65 10.00 14.65 15.50 13.89 13.91
relations:NNS 10.31 12.24 20.00   5.11 12.42   5.11 10.00   4.90   7.48
various:JJ 12.50 10.52 20.00 10.80 11.73 10.80 12.50 12.00   9.52
countries:NNS   9.44 13.73 20.00   4.97 14.91   4.97   5.33   6.32   8.51
the:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50 20.00 20.00
world:NN 10.11 15.00 20.00   8.91 15.00   8.91   9.60   8.10   9.12
NO_WORD 10.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.53 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  0.44 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Iraqi" of "diplomacy" dropped on aligned hyp word "diplomacy"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 4.4053
Threshold: -1.8794


Inference ID: 2110

Txt: Journalists of the concealed BBC, that was made happen through advisers of London industralists, asked Slavkov.

Hyp: BBC undercover reporters, posing as consultants acting for London businessmen, ask Slavkov. (don't know)

BBC
NNP
undercover
JJ
reporters
NNS
posing
VBG
consultants
NNS
acting
VBG
London
NNP
businessmen
NNS
ask
VBP
Slavkov
NNP
Journalists:NNS 10.50 12.00   5.23 15.00   7.06 15.00   8.21   8.83 15.00 10.50
the:DT 20.50 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50
concealed:NNP 10.50   9.14   8.85 13.09   8.53 15.00   9.01   9.09 14.24 10.50
BBC:NNP   0.00 12.50 10.50 15.50 10.50 15.50 10.50 10.50 15.50 10.50
that:WDT 20.50 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50
was:VBD 15.50 12.00 15.00 10.00 15.00 10.00 15.50 15.00 10.00 15.50
made:VBN 15.50 10.74 12.51   9.72 14.23   7.76 15.50 14.17 10.00 15.50
happen:VB 15.50 12.00 14.56 10.00 15.00   9.89 15.50 15.00   6.04 15.50
advisers:NNS 10.50 12.00   6.51 14.38   0.00 15.00   8.45   8.26 13.87 10.50
London:NNP 10.50 12.50   8.85 15.50   8.45 15.50   0.00   9.48 15.50 10.50
industralists:NNS 10.50 12.00 10.00 15.00 10.00 15.00 10.50 10.00 15.00 10.50
asked:VBD 15.50 10.99 12.09   5.07 14.55   9.32 15.50 14.64   0.00 15.50
Slavkov:NNP 10.50 12.50 10.50 15.50 10.50 15.50 10.50 10.50 15.50   0.00
NO_WORD 10.00   9.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.75 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added acting[acting-VBG]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "made" of "Journalists" dropped on aligned hyp word "reporters"
-1.00  1.00 NullPunisher.other : acting
-1.00  1.00 NullPunisher.other : undercover
-1.00  1.00 NullPunisher.other : posing
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.2462
Threshold: -1.8794


Inference ID: 1149

Txt: The American State Department announced that Russia recalled her ambassador to the United States "for consultation" due to the bombing operations on Iraq.

Hyp: The American Ministry of Foreign Affairs announced that Russia called the United States about the bombings on Iraq. (don't know)

The
DT
American_Ministry_of_Foreign_Affairs
NNP
announced
VBD
that
IN
Russia
NNP
called
VBD
the
DT
United_States
NNP
the
DT
bombings
NNS
Iraq
NNP
The:DT   0.00 20.50 20.00 20.00 20.50 20.00   0.00 20.50   0.00 20.00 20.50
American_State_Department:NNP 20.50   6.28 15.50 20.50   6.23 15.50 20.50   3.20 20.50   9.28   6.62
announced:VBD 20.00 15.50   0.00 20.00 15.50   6.09 20.00 15.50 20.00 14.14 15.50
that:IN 20.00 20.50 20.00   0.00 20.50 20.00 20.00 20.50 20.00 20.00 20.50
Russia:NNP 20.50   8.86 15.50 20.50   0.00 15.50 20.50   4.67 20.50 10.21   6.27
recalled:VBD 20.00 15.50   8.71 20.00 15.50   4.88 20.00 15.50 20.00 14.72 15.50
her:PRP$ 20.00 12.50 15.00 20.00 12.50 15.00 20.00 12.50 20.00 12.00 12.50
ambassador:NN 20.00   8.40 14.85 20.00   9.83 15.00 20.00   8.62 20.00   9.31   9.87
the:DT   0.00 20.50 20.00 20.00 20.50 20.00   0.00 20.50   0.00 20.00 20.50
United_States:NNP 20.50   6.96 15.50 20.50   4.67 15.50 20.50   0.00 20.50   9.37   5.14
consultation:NN 20.00   8.61 13.56 20.00   9.63 12.50 20.00   8.24 20.00   9.42   9.90
the:DT   0.00 20.50 20.00 20.00 20.50 20.00   0.00 20.50   0.00 20.00 20.50
bombing:NN 20.00   9.60 15.00 20.00 10.21 14.16 20.00   9.37 20.00   0.00 10.34
operations:NNS 20.00   8.28 15.00 20.00   9.43 15.00 18.79   7.88 18.79   8.54   9.73
Iraq:NNP 20.50   9.26 15.50 20.50   6.27 15.50 20.50   5.14 20.50 10.34   0.00
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.23 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  8.00 Alignment.hypSpan
 0.10  0.55 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "consultation" of "United_States" dropped on aligned hyp word "United_States"
-0.10  1.00 NullPunisher.article : the
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.7916
Threshold: -1.8794


Inference ID: 1148

Txt: The American television network "A.B.C." quoted undisclosed sources at the American Defense Department today, Thursday, saying that the intense bombing operations on Iraq may cease by the end of this week.

Hyp: The television network "CNN", quoting sources in the American Ministry of Defence, revealed that the bombing of Iraq might end this week. (don't know)

The
DT
television
NN
network
NN
CNN
NNP
quoting
VBG
sources
NNS
the
DT
American_Ministry_of_Defence
NNP
revealed
VBD
that
IN
the
DT
bombing
NN
Iraq
NNP
might
MD
end
VB
this
DT
week
NN
The:DT   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00   0.00 20.00 20.50 10.00 20.00 10.00 20.00
American:JJ 20.00 12.00 12.00 12.00 12.00 12.00 20.00   7.50 12.00 20.00 20.00 12.00 12.50 20.00 12.00 20.00 12.00
television:NN 20.00   0.00   2.69   8.54 14.14   7.19 20.00   7.52 13.89 20.00 20.00   9.17   9.40 20.00 15.00 20.00   7.67
network:NN 20.00   2.69   0.00   8.49 15.00   8.03 20.00   8.52 13.96 20.00 20.00   9.38   9.85 20.00 15.00 20.00   7.98
A.B.C.:NNP 20.00   8.62   8.85 10.00 15.00   8.46 20.00   8.96 15.00 20.00 20.00   9.59 10.07 20.00 15.00 20.00   8.43
quoted:VBD 20.00 14.39 14.05 15.00   0.00 14.74 20.00 15.50   7.93 20.00 20.00 15.00 15.50 20.00 10.00 20.00 15.00
undisclosed:JJ 20.00 11.74 11.11 12.00 10.80   9.86 20.00 12.50   8.75 20.00 20.00 12.00 12.50 20.00 11.60 20.00 11.53
sources:NNS 20.00   7.19   8.03 10.00 12.81   0.00 20.00   7.45 10.56 20.00 20.00   8.74   7.95 20.00 15.00 20.00   7.46
the:DT   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00   0.00 20.00 20.50 10.00 20.00 10.00 20.00
American_Defense_Department:NNP 20.50   7.52   8.52 10.50 15.50   7.45 20.50   0.00 15.50 20.50 20.50   9.60   9.26 20.50 15.50 20.50   7.95
today:NN 20.50   8.56   8.84 10.50 14.14   8.37 20.50   8.36 14.10 20.50 20.50   9.80   9.77 20.50 15.50 20.50   4.61
Thursday:NNP 20.50   8.88   9.13 10.50 15.50   8.71 20.50   8.70 15.50 20.50 20.50   9.97   9.94 20.50 15.50 20.50   6.21
saying:VBG 20.00 15.00 15.00 15.00   4.44 14.37 20.00 15.50   8.10 20.00 20.00 13.76 15.50 20.00 10.00 20.00 13.36
that:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.00   0.00 20.00 20.00 20.50 20.00 20.00 18.04 20.00
the:DT   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00   0.00 20.00 20.50 10.00 20.00 10.00 20.00
intense:JJ 20.00   8.65 10.35 12.00 10.62 10.62 20.00 12.50 10.43 20.00 20.00 12.00 12.50 20.00 12.00 20.00 10.37
bombing:NN 20.00   9.17   9.38 10.00 14.67   8.74 20.00   9.60 13.89 20.00 20.00   0.00 10.34 20.00 14.38 20.00   9.08
operations:NNS 20.00   7.98   8.27 10.00 15.00   7.79 18.79   8.28 15.00 20.00 18.79   8.54   9.73 20.00 15.00 20.00   7.74
Iraq:NNP 20.50   9.40   9.85 10.50 15.50   7.95 20.50   9.26 15.50 20.50 20.50 10.34   0.00 20.50 15.50 20.50   9.55
may:MD 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.50 20.00 20.00 10.00 20.00 20.50 10.00 20.00 10.00 20.00
cease:VB 20.00 15.00 15.00 15.00   8.17 15.00 20.00 15.50   8.81 20.00 20.00 13.72 15.50 20.00   7.95 20.00 13.70
the:DT   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.50 20.00 20.00   0.00 20.00 20.50 10.00 20.00 10.00 20.00
end:NN 20.00   7.09   7.94 10.00 15.00   5.31 20.00   7.34 14.02 20.00 20.00   9.06   7.92 20.00   0.00 20.00   7.36
this:DT 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.50 20.00 18.04 10.00 20.00 20.50 10.00 20.00   0.00 20.00
week:NN 20.00   7.67   7.98 10.00 13.25   7.46 20.00   7.95 12.72 20.00 20.00   9.08   9.55 20.00 15.00 20.00   0.00
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00   1.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.29 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  6.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.24 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "undisclosed" of "sources" dropped on aligned hyp word "sources"
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.functionWord : that
-0.05  1.00 NullPunisher.aux : might
-1.00  1.00 NullPunisher.other : CNN
-1.00  1.00 NullPunisher.other : revealed
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.unalignedRoot : "revealed" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.4292
Threshold: -1.8794


Inference ID: 2098

Txt: U.S. consumer spending dived in June.

Hyp: The cost of the consumer of the United States fell in June. (don't know)

The
DT
cost
NN
the
DT
consumer
NN
the
DT
United_States
NNP
fell
VBD
June
NNP
U.S.:NNP 20.50   6.66 20.50   7.52 20.50   0.00 15.50   8.35
consumer:NN 20.00   6.26 20.00   0.00 20.00   6.72 14.43   8.41
spending:NN 20.00   6.89 20.00   7.46 20.00   8.17 14.70   9.04
dived:VBD 20.00 15.00 20.00 15.00 20.00 15.50   7.80 15.50
June:NNP 20.50   7.71 20.50   8.41 20.50   8.21 15.50   0.00
NO_WORD   1.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.98 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.62 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 06/01/1000
-0.10  1.00 NullPunisher.article : the
-0.10  1.00 NullPunisher.article : The
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "fell" aligned badly to "dived"
Hand-tuned score (dot product of above): 0.1234
Threshold: -1.8794


Inference ID: 1211

Txt: The Croatian will face competition, especially from the American of Chinese origin, Michael Chang, second-seeded in the tournament. Organizers hope that the tournament will contribute to advancing tennis in their countries.

Hyp: The Croatian and the Asian-American, Michael Chang (number two) will play eachother, and hopefully popularise the game in their native countries. (yes)

The
DT
Croatian
NNP
and
CC
the
DT
Asian-American
NNP
Michael_Chang
NNP
number
NN
two
CD
will
MD
play
VB
eachother
NN
hopefully
RB
popularize
VB
the
DT
game
NN
their
PRP$
native
JJ
countries
NNS
The:DT   0.00 20.00 10.00   0.00 20.50 20.50 20.00 20.50 10.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
Croatian:NNP 20.00   0.00 20.00 20.00 10.50 10.50 10.00 20.50 20.00 15.00 10.00 15.00 15.00 20.00 10.00 12.00 12.00 10.00
will:MD 10.00 20.00 10.00 10.00 20.50 20.50 20.00 20.50   0.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
face:VB 20.00 15.00 20.00 20.00 15.50 15.50 13.92 19.22 20.00   7.23 15.00 20.00   9.83 20.00 14.20 15.00 12.00 15.00
competition:NN 20.00 10.00 20.00 20.00   9.11   9.24   7.55 18.71 20.00 14.25 10.00 15.00 13.02 20.00   8.56 12.00 12.00   7.47
especially:RB 20.00 15.00 20.00 20.00 15.50 15.50 11.40 20.09 20.00 17.82 15.00   8.67 18.69 20.00 14.64 15.00 11.68 13.57
the:DT   0.00 20.00 10.00   0.00 20.50 20.50 20.00 20.50 10.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
American:NNP 20.00 10.00 20.00 20.00   0.50   8.67   7.54 20.50 20.00 15.00 10.00 15.00 15.00 20.00   8.14 12.00 12.00   5.97
Chinese:JJ 20.00 12.00 20.00 20.00 12.50 12.50 12.00 20.50 20.00 12.00 12.00 12.00 12.00 20.00 12.00 15.00 10.00 12.00
origin:NN 20.00 10.00 20.00 20.00   8.03   8.68   7.55 20.50 20.00 15.00 10.00 15.00 12.73 20.00   8.15 12.00   7.78   4.22
Michael_Chang:NNP 20.50 10.50 20.50 20.50   9.10   0.00   8.71 20.50 20.50 15.50 10.50 15.50 15.50 20.50   9.20 12.50 12.50   7.95
second-seeded:JJ 20.00 12.00 20.00 20.00 12.50 12.50 11.55 20.50 20.00   9.56 12.00 11.12 12.00 20.00   8.87 15.00   9.98 11.18
the:DT   0.00 20.00 10.00   0.00 20.50 20.50 20.00 20.50 10.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
tournament:NN 20.00 10.00 20.00 20.00   9.77   9.86   8.37 19.45 20.00 10.66 10.00 11.97 15.00 20.00   3.94 12.00 10.24   8.45
Organizers:NNS 20.00 10.00 20.00 20.00   8.82   9.82   8.96 20.50 20.00 15.00 10.00 15.00 15.00 20.00   9.31 12.00 12.00   7.97
hope:VBP 20.00 15.00 20.00 20.00 15.50 15.50 14.01 20.18 20.00   6.64 15.00 14.70 10.00 20.00 13.31 15.00 12.00 14.82
that:IN 20.00 20.00 20.00 20.00 20.50 20.50 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
the:DT   0.00 20.00 10.00   0.00 20.50 20.50 20.00 20.50 10.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
tournament:NN 20.00 10.00 20.00 20.00   9.77   9.86   8.37 19.45 20.00 10.66 10.00 11.97 15.00 20.00   3.94 12.00 10.24   8.45
will:MD 10.00 20.00 10.00 10.00 20.50 20.50 20.00 20.50   0.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00
contribute:VB 20.00 15.00 20.00 20.00 15.50 15.50 14.14 20.50 20.00   8.62 15.00 20.00   8.44 20.00 14.89 15.00 11.78 13.49
advancing:VBG 20.00 15.00 20.00 20.00 15.50 15.50 14.12 20.50 20.00   9.21 15.00 20.00   7.77 20.00 14.68 15.00 11.63 14.72
tennis:NN 20.00 10.00 20.00 20.00   9.76   9.84   8.99 19.73 20.00 12.90 10.00 13.08 15.00 20.00   7.87 12.00 11.15   8.43
their:PRP$ 20.00 12.00 20.00 20.00 12.50 12.50 12.00 18.43 20.00 15.00 12.00 20.00 15.00 20.00 12.00   0.00 15.00 12.00
countries:NNS 20.00 10.00 20.00 20.00   7.17   7.95   6.69 19.78 20.00 15.00 10.00 15.00 14.84 20.00   7.41 12.00 10.01   0.00
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00   9.00 10.00   1.00 10.00 10.00   9.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.77 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  9.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added native[native-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "origin" of "American" dropped on aligned hyp word "Asian-American"
-1.00  1.00 NullPunisher.other : native
-3.00  1.00 NullPunisher.entity : two
-1.00  1.00 NullPunisher.other : eachother
-1.00  1.00 NullPunisher.other : popularize
-1.00  1.00 NullPunisher.other : number
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : and
-1.00  1.00 NullPunisher.other : game
-0.05  1.00 NullPunisher.aux : will
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "play" aligned badly to "Organizers"
-3.00  1.00 Structure.argsMismatch : popularize (verbal compl of the root) not aligned
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Croatian" <-nsubj-- "face" vs. hyp "Croatian" <-tmod-- "play", which aligned to text "Organizers" args have different parents, different relations: text "Michael_Chang" <-dep-- "face" vs. hyp "Michael_Chang" <-nsubj-- "play", which aligned to text "Organizers"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -21.1145
Threshold: -1.8794


Inference ID: 1165

Txt: Aziz confirmed that Iraq's demands include "the lifting of the blockade and the restructuring of the espionage commission, including its Chairman, (Richard) Butler.

Hyp: Iraq demands the lifting of the embargo and changes in the committee. (yes)

Iraq
NNP
demands
VBZ
the
DT
lifting
NN
the
DT
embargo
NN
changes
NNS
the
DT
committee
NN
Aziz:NNP 10.50 15.50 20.50 10.50 20.50 10.50 10.50 20.50 10.50
confirmed:VBD 15.50   9.02 20.00 15.00 20.00 15.00 14.16 20.00 13.28
that:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
Iraq:NNP   0.00 15.50 20.50   9.84 20.50   9.79   9.24 20.50   9.47
demands:NNS   9.71   0.00 20.00   8.11 20.00   8.31   7.19 20.00   7.56
include:VBP 15.50   8.47 20.00 14.20 20.00 14.91 13.90 20.00 13.78
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00   0.00 20.00
lifting:NN   9.84 13.11 20.00   0.00 20.00   2.66   7.47 20.00   7.82
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00   0.00 20.00
blockade:NN 10.04 13.82 20.00   5.31 20.00   3.87   6.26 20.00   8.26
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00   0.00 20.00
restructuring:NN   9.93 12.72 20.00   6.28 20.00   9.36   5.57 20.00   8.01
the:DT 20.50 20.00   0.00 20.00   0.00 20.00 20.00   0.00 20.00
espionage:NN 10.37 13.27 20.00   9.42 20.00   7.03   8.87 20.00   9.08
commission:NN   9.47 15.00 20.00   7.82 20.00   8.88   6.70 20.00   0.00
including:VBG 15.50   8.92 20.00 14.39 20.00 15.00 14.34 20.00 14.18
its:PRP$ 12.50 15.00 20.00 12.00 20.00 12.00 12.00 20.00 12.00
Chairman:NN   9.05 15.00 20.00   7.71 20.00   8.80   6.55 20.00   6.97
Richard:NNP 10.50 15.00 20.00 10.00 20.00 10.00 10.00 20.00 10.00
Butler:NNP   9.88 15.50 20.50   9.67 20.50 10.21   9.00 20.50   9.25
NO_WORD 10.00 10.00   1.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.07 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.44 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "espionage" of "commission" dropped on aligned hyp word "committee"
-1.00  1.00 NullPunisher.other : changes
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "lifting" <-dobj-- "include" vs. hyp "lifting" <-dobj-- "demands", which aligned to text "demands" text "Iraq" is poss of "demands" while hyp "Iraq" is nsubj of "demands" which aligned to text "demands"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.4975
Threshold: -1.8794


Inference ID: 1986

Txt: The girl was found in Drummondville.

Hyp: Drummondville contains the girl. (don't know)

Drummondville
NNP
contains
VBZ
the
DT
girl
NN
The:DT 20.50 20.00   0.00 20.00
girl:NN 10.50 15.00 20.00   0.00
was:VBD 15.50 10.00 20.00 15.00
found:VBN 15.50   7.96 20.00 13.42
Drummondville:NNP   0.50 15.50 20.50 10.50
NO_WORD 10.00 10.00   1.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.18 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : contains
-2.00  1.00 RootEntailment.unalignedRoot : "contains" not aligned to anything
Hand-tuned score (dot product of above): -0.7681
Threshold: -1.8794


Inference ID: 1433

Txt: The report catalogues 10 missed opportunities within the CIA and FBI to uncover pieces of the September 11 plot.

Hyp: The report analyzes 10 missed opportunities within the CIA and FBI to uncover pieces of the September 11 plot. (yes)

The
DT
report
NN
analyzes
VBZ
10
CD
missed
VBN
opportunities
NNS
the
DT
CIA
NNP
FBI
NNP
to
TO
uncover
VB
pieces
NNS
the
DT
September_11
NN
plot
NN
The:DT   0.00 20.00 20.00 20.50 20.00 20.00   0.00 20.50 20.50 10.00 20.00 20.00   0.00 20.50 20.00
report:NN 20.00   0.00 13.13 20.50 14.13   7.40 20.00   9.27   9.27 20.00 14.12   7.47 20.00   7.62   8.20
catalogues:VBZ 20.00 15.00   7.99 20.50 10.00 15.00 20.00 15.50 15.50 20.00   9.41 11.86 20.00 15.50 15.00
10:CD 20.50 20.50 20.50   0.00 19.11 20.50 20.50 20.50 20.50 20.50 20.29 20.28 20.50 12.73 20.50
missed:VBN 20.00 14.13   9.11 19.11   0.00 13.56 20.00 15.50 15.50 20.00   9.46 14.52 20.00 12.90 15.00
opportunities:NNS 20.00   7.40 14.99 20.50 13.56   0.00 20.00   8.96   8.96 20.00 15.00   7.00 20.00   7.95   7.82
the:DT   0.00 20.00 20.00 20.50 20.00 20.00   0.00 20.50 20.50 10.00 20.00 20.00   0.00 20.50 20.00
CIA:NNP 20.50   9.27 15.50 20.50 15.50   8.96 20.50   0.00   4.56 20.50 15.50   9.02 20.50   9.31   9.54
FBI:NNP 20.50   9.27 15.50 20.50 15.50   8.96 20.50   4.56   0.00 20.50 15.50   9.02 20.50   9.31   9.54
to:TO 10.00 20.00 20.00 20.50 20.00 20.00 10.00 20.50 20.50   0.00 20.00 20.00 10.00 20.50 20.00
uncover:VB 20.00 14.12   6.08 20.29   9.46 15.00 20.00 15.50 15.50 20.00   0.00 12.13 20.00 15.41 13.44
pieces:NNS 20.00   7.47 13.14 20.28 14.52   7.00 20.00   9.02   9.02 20.00 12.13   0.00 20.00   8.03   7.89
the:DT   0.00 20.00 20.00 20.50 20.00 20.00   0.00 20.50 20.50 10.00 20.00 20.00   0.00 20.50 20.00
September_11:NN 20.50   7.62 15.50 12.73 12.90   7.95 20.50   9.31   9.31 20.50 15.41   8.03 20.50   0.00   8.75
plot:NN 20.00   8.20 15.00 20.50 15.00   7.82 20.00   9.54   9.54 20.00 13.44   7.89 20.00   8.75   0.00
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.78 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  0.93 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 09/11/1000
-0.10  1.00 NullPunisher.functionWord : to
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "analyzes" aligned badly to "catalogues"
Hand-tuned score (dot product of above): 2.0161
Threshold: -1.8794


Inference ID: 1970

Txt: The witness then spotted Rodriguez crouching behind a gate for protection.

Hyp: The witness rescued Rodriguez. (don't know)

The
DT
witness
NN
rescued
VBD
Rodriguez
NNP
The:DT   0.00 20.00 20.00 20.50
witness:NN 20.00   0.00 13.60 10.50
then:RB 20.00 15.00 20.00 15.50
spotted:VBD 20.00 11.84   7.35 15.50
Rodriguez:NNP 20.50 10.50 15.50   0.00
crouching:VBG 20.00 13.03   7.96 15.50
a:DT 10.00 20.00 20.00 20.50
gate:NN 20.00   7.86 14.40 10.50
protection:NN 20.00   8.42 14.47 10.50
NO_WORD   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "crouching" of "Rodriguez" dropped on aligned hyp word "Rodriguez"
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "rescued" aligned badly to "spotted"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.8233
Threshold: -1.8794


Inference ID: 1977

Txt: His family has steadfastly denied the charges.

Hyp: The charges were denied by his family. (yes)

The
DT
charges
NNS
were
VBD
denied
VBN
his
PRP$
family
NN
His:PRP$ 20.00 12.00 15.00 15.00   0.00 12.00
family:NN 20.00   7.43 15.00 13.04 12.00   0.00
has:VBZ 20.00 15.00 10.00 10.00 15.00 15.00
steadfastly:RB 20.00 13.13 20.00 14.90 15.00 12.97
denied:VBN 20.00   9.13 10.00   0.00 15.00 13.04
the:DT   0.00 20.00 20.00 20.00 20.00 20.00
charges:NNS 20.00   0.00 15.00   9.13 12.00   7.43
NO_WORD   1.00 10.00   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.66 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "steadfastly" of "denied" dropped on aligned hyp word "denied"
-0.05  1.00 NullPunisher.aux : were
-2.00  1.00 Person.mismatch : person mimatch between his and His
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.7965
Threshold: -1.8794


Inference ID: 1429

Txt: Alcohol now fuels 44 percent of all violent crime and 70 percent of Accident and Emergency hospital admissions at peak times are due to booze.

Hyp: Alcohol has an effect on violent crimes. (yes)

Alcohol
NNP
has
VBZ
an
DT
effect
NN
violent
JJ
crimes
NNS
Alcohol:NNP   0.00 15.00 20.00   7.14 12.00   7.58
now:RB 15.00 20.00 20.00 15.00 12.00 15.00
fuels:VBD 15.00 10.00 20.00 15.00 12.00 14.83
44:CD 20.50 20.50 20.50 20.50 18.81 18.69
percent:NN   8.78 15.50 20.50   8.12 11.98   8.50
all:DT 20.00 20.00 10.00 20.00 20.00 20.00
violent:JJ 12.00 12.00 20.00 11.13   0.00   4.51
crime:NN   7.58 15.00 20.00   6.77   4.57   0.00
70:CD 20.50 20.50 20.50 20.50 19.41 20.01
percent:NN   8.78 15.50 20.50   8.12 11.98   8.50
Accident:NNP   8.18 15.00 20.00   7.50 12.00   7.90
Emergency:NNP   8.44 15.00 20.00   7.81 12.00   8.17
hospital:NN   7.68 15.00 20.00   7.46 11.42   7.86
admissions:NNS   8.94 15.00 20.00   8.14 11.05   7.94
peak:JJ 12.00 12.00 20.00 10.14   8.52 11.16
times:NNS   8.13 15.00 20.00   7.44 11.58   5.00
are:VBP 15.00   8.78 20.00 15.00 12.00 15.00
due:JJ 12.00 12.00 20.00 11.07 10.00 12.00
booze:NN   0.75 15.00 20.00   7.57 11.23   7.96
NO_WORD 10.00 10.00   1.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.04 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : has
-1.00  1.00 NullPunisher.other : effect
-0.10  1.00 NullPunisher.article : an
-2.00  1.00 RootEntailment.unalignedRoot : "has" not aligned to anything
Hand-tuned score (dot product of above): -1.2969
Threshold: -1.8794


Inference ID: 1435

Txt: Blair has sympathy for anyone who has lost their lives in Iraq.

Hyp: Blair is sympathetic to anyone who has lost their lives in Iraq. (yes)

Blair
NNP
is
VBZ
sympathetic
JJ
anyone
NN
who
WP
has
VBZ
lost
VBN
their
PRP$
lives
NNS
Iraq
NNP
Blair:NNP   0.00 15.50 12.50 10.50 12.50 15.50 15.50 12.50   9.64   9.88
has:VBZ 15.50   8.64 12.00 15.00 15.00   0.00 10.00 15.00 15.00 15.50
sympathy:NN 10.13 15.00   6.12 10.00 12.00 15.00 13.53 12.00   5.98 10.22
anyone:NN 10.50 15.00 12.00   0.00 12.00 15.00 15.00 12.00 10.00 10.50
who:WP 12.50 15.00 15.00 12.00   0.00 15.00 15.00 10.00 12.00 12.50
has:VBZ 15.50   8.64 12.00 15.00 15.00   0.00 10.00 15.00 15.00 15.50
lost:VBN 15.50 10.00 12.00 15.00 15.00 10.00   0.00 15.00 13.87 15.50
their:PRP$ 12.50 15.00 15.00 12.00 10.00 15.00 15.00   0.00 12.00 12.50
lives:NNS   9.64 15.00   9.31 10.00 12.00 15.00 13.87 12.00   0.00   9.79
Iraq:NNP   9.88 15.50 12.50 10.50 12.50 15.50 15.50 12.50   9.79   0.00
NO_WORD 10.00   1.00   9.00 10.00   1.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.64 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.functionWord : who
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "sympathetic" aligned badly to "sympathy"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Blair" <-nsubj-- "has" vs. hyp "Blair" <-nsubj-- "sympathetic", which aligned to text "sympathy" text "anyone" is prep_for of "sympathy" while hyp "anyone" is prep_to of "sympathetic" which aligned to text "sympathy"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -5.2001
Threshold: -1.8794


Inference ID: 2051

Txt: Protesters, many from organized pro-government groups but including many ordinary citizens, carried anti-American banners and chanted slogans attacking U.N. Secretary-General Kofi Annan for his close alignment with U.S. policy.

Hyp: Protesters confiscated anti-American banners and chanted slogans attacking U.N. Secretary-General Kofi Annan for his close alignment with U.S. policy. (don't know)

Protesters
NNS
confiscated
VBD
anti-American
JJ
banners
NNS
chanted
VBD
slogans
NNS
attacking
VBG
U.N.
NNP
Secretary-General
NNP
Kofi_Annan
NNP
his
PRP$
close
RB
alignment
JJ
U.S.
NNP
policy
NN
Protesters:NNS   0.00 15.00 12.00   8.77 15.00   8.78 15.00   9.22   8.24 10.50 12.00 15.00 12.00   7.58   7.29
many:JJ 12.00 12.00 10.00 12.00 12.00 12.00 12.00 12.50 12.00 12.50 13.71 12.00 10.00 12.50 12.00
organized:JJ 12.00 10.13   9.33   8.26   8.70   7.85   9.92 12.50 12.00 12.50 15.00 11.83   9.62 12.50 11.59
pro-government:JJ 12.00 10.62   9.00 11.71 12.00 11.35 12.00 12.50 12.00 12.50 15.00 12.00   9.10 12.50 11.05
groups:NNS   5.19 13.78 12.00   6.45 12.78   6.35 13.78   7.88   7.96 10.50 12.00 15.00 10.63   5.50   5.29
including:VBG 15.00   8.01 12.00 13.26   9.62 14.57   7.30 15.50 15.00 15.50 15.00 20.00 11.32 15.50 15.00
many:JJ 12.00 12.00 10.00 12.00 12.00 12.00 12.00 12.50 12.00 12.50 13.71 12.00 10.00 12.50 12.00
ordinary:JJ 12.00 10.17 10.00 11.07 11.88 10.45 11.75 12.50 12.00 12.50 15.00 10.97   9.90 12.50 11.82
citizens:NNS   6.85 11.55 12.00   7.89 13.21   6.69 13.31   9.72   8.44 10.50 12.00 15.00 11.89   8.42   8.09
carried:VBD 15.00   7.07 12.00 14.15   8.12 13.38   5.44 15.50 15.00 15.50 15.00 20.00 10.54 15.50 15.00
anti-American:JJ 12.00 12.00   0.00 12.00 12.00 12.00 12.00 12.50 12.00 12.50 15.00 12.00 10.00 12.50 12.00
banners:NNS   8.77 14.80 12.00   0.00   9.92   4.34 13.14 10.30   9.27 10.50 12.00 15.00 10.63   9.60   9.20
chanted:VBD 15.00   8.39 12.00   9.92   0.00 10.14   7.79 15.50 15.00 15.50 15.00 19.54 11.06 15.50 14.75
slogans:NNS   8.78 14.79 12.00   4.34 10.14   0.00 12.04 10.12   9.79 10.50 12.00 13.79 10.84   9.19   8.82
attacking:VBG 15.00   9.06 12.00 13.14   7.79 12.04   0.00 15.50 15.00 15.50 15.00 19.45 11.76 15.50 13.56
U.N.:NNP   9.22 15.50 12.50 10.30 15.50 10.12 15.50   0.00   9.23 10.50 12.50 15.50 12.50   7.70   9.27
Secretary-General:NNP   8.24 15.00 12.00   9.27 15.00   9.79 15.00   9.23   0.00 10.50 12.00 15.00 12.00   9.52   9.13
Kofi_Annan:NNP 10.50 15.50 12.50 10.50 15.50 10.50 15.50 10.50 10.50   0.00 12.50 15.50 12.50 10.50 10.50
his:PRP$ 12.00 15.00 15.00 12.00 15.00 12.00 15.00 12.50 12.00 12.50   0.00 20.00 15.00 12.50 12.00
close:RB 15.00 20.00 12.00 15.00 19.54 13.79 19.45 15.50 15.00 15.50 15.00   0.00 12.00 15.50 15.00
alignment:JJ 12.00 12.00 10.00 10.63 11.06 10.84 11.76 12.50 12.00 12.50 15.00 12.00   0.00 12.50 12.00
U.S.:NNP   7.58 15.50 12.50   9.60 15.50   9.19 15.50   7.70   9.52 10.50 12.50 15.50 12.50   0.00   7.65
policy:NN   7.29 14.60 12.00   9.20 14.75   8.82 13.56   9.27   9.13 10.50 12.00 15.00 12.00   7.65   0.00
NO_WORD 10.00 10.00   9.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00   9.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.17 Alignment.score
 1.00  0.96 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  15.00 Alignment.hypSpan
 0.10  0.93 Alignment.txtSpan
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "confiscated" aligned badly to "carried"
Hand-tuned score (dot product of above): 2.7189
Threshold: -1.8794


Inference ID: 1434

Txt: The report catalogues 10 missed opportunities within the CIA and FBI to uncover pieces of the September 11 plot.

Hyp: Ten missed opportunities within the CIA and FBI are uncovered in the report. (yes)

Ten
CD
missed
VBN
opportunities
NNS
the
DT
CIA
NNP
FBI
NNP
are
VBP
uncovered
VBN
the
DT
report
NN
The:DT 20.50 20.00 20.00   0.00 20.50 20.50 20.00 20.00   0.00 20.00
report:NN 20.50 14.13   7.40 20.00   9.27   9.27 15.00 12.48 20.00   0.00
catalogues:VBZ 20.50 10.00 15.00 20.00 15.50 15.50 10.00   9.55 20.00 15.00
10:CD   0.00 19.11 20.50 20.50 20.50 20.50 20.50 20.15 20.50 20.50
missed:VBN 20.50   0.00 13.56 20.00 15.50 15.50 10.00   9.00 20.00 14.13
opportunities:NNS 20.50 13.56   0.00 20.00   8.96   8.96 15.00 15.00 20.00   7.40
the:DT 20.50 20.00 20.00   0.00 20.50 20.50 20.00 20.00   0.00 20.00
CIA:NNP 20.50 15.50   8.96 20.50   0.00   4.56 15.50 15.50 20.50   9.27
FBI:NNP 20.50 15.50   8.96 20.50   4.56   0.00 15.50 15.50 20.50   9.27
to:TO 20.50 20.00 20.00 10.00 20.50 20.50 20.00 20.00 10.00 20.00
uncover:VB 20.50   9.46 15.00 20.00 15.50 15.50 10.00   0.00 20.00 14.12
pieces:NNS 20.50 14.52   7.00 20.00   9.02   9.02 15.00 12.74 20.00   7.47
the:DT 20.50 20.00 20.00   0.00 20.50 20.50 20.00 20.00   0.00 20.00
September_11:NN 20.50 12.90   7.95 20.50   9.31   9.31 15.50 15.06 20.50   7.62
plot:NN 20.50 15.00   7.82 20.00   9.54   9.54 15.00 13.89 20.00   8.20
NO_WORD 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.45 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.90 Alignment.txtSpan
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-0.05  1.00 NullPunisher.aux : are
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "opportunities" <-dobj-- "catalogues" vs. hyp "opportunities" <-nsubjpass-- "uncovered", which aligned to text "uncover" args have different parents, different relations: text "report" <-nsubj-- "catalogues" vs. hyp "report" <-prep_in-- "uncovered", which aligned to text "uncover"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -2.0947
Threshold: -1.8794


Inference ID: 1431

Txt: The report faults intelligence agencies for a "lack of imagination" in not anticipating that al Qaeda could attack the United States using hijacked aircraft.

Hyp: The report blames intelligence agencies. (yes)

The
DT
report
NN
blames
VBZ
intelligence_agencies
NNS
The:DT   0.00 20.00 20.00 20.00
report:NN 20.00   0.00 14.72   8.39
faults:VBD 20.00 15.00   9.28 13.87
intelligence_agencies:NNS 20.00   8.39 15.00   0.00
a:DT 10.00 20.00 20.00 20.00
lack:NN 20.00   7.75 11.26   8.78
imagination:NN 20.00   7.43 15.00   8.06
in:IN 20.00 20.00 20.00 20.00
not:RB 20.00 15.00 20.00 15.00
anticipating:VBG 20.00 12.22   8.92 15.00
that:IN 20.00 20.00 20.00 20.00
al:NNP 20.00   8.65 15.00   9.07
Qaeda:NNP 20.50   8.85 15.50   8.26
could:MD 10.00 20.00 20.00 20.00
attack:VB 20.00 15.00   7.82 15.00
the:DT   0.00 20.00 20.00 20.00
United_States:NNP 20.50   7.75 15.50   8.41
using:VBG 20.00 15.00   8.79 15.00
hijacked:JJ 20.00 11.38   9.93 12.00
aircraft:NN 20.00   8.13 15.00   8.64
NO_WORD   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "lack" of "intelligence_agencies" dropped on aligned hyp word "intelligence_agencies"
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "blames" aligned badly to "faults"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.8250
Threshold: -1.8794


Inference ID: 1438

Txt: Iraq would have been an unsafe, unsecure country that did pose a threat to the world.

Hyp: A threat to the world would have arisen from Iraq. (yes)

A
DT
threat
NN
the
DT
world
NN
would
MD
have
VB
arisen
VBN
Iraq
NNP
Iraq:NNP 20.50   9.81 20.50   9.60 20.50 15.50 15.50   0.00
would:MD 10.00 20.00 10.00 18.00   0.00 18.69 20.00 20.50
have:VB 20.00 15.00 20.00 15.00 18.69   0.00 10.00 15.50
been:VBN 20.00 15.00 20.00 15.00 20.00 10.00 10.00 15.50
an:DT 10.00 20.00 10.00 20.00 10.00 20.00 20.00 20.50
unsafe:JJ 20.00   9.89 20.00 11.16 20.00 12.00   8.75 12.50
unsecure:JJ 20.00 12.00 20.00 12.00 20.00 12.00 12.00 12.50
country:NN 20.00   6.97 20.00   5.47 20.00 15.00 13.26   5.33
that:WDT 10.00 18.00 10.00 20.00 10.00 20.00 20.00 20.50
did:VBD 20.00 14.42 20.00 14.97 18.57 10.00   9.12 15.50
pose:VB 20.00 10.09 20.00 14.47 20.00   8.51   5.93 15.50
a:DT   0.00 20.00 10.00 20.00 10.00 20.00 20.00 20.50
threat:NN 20.00   0.00 20.00   7.48 20.00 15.00 12.01   9.81
the:DT 10.00 20.00   0.00 20.00 10.00 20.00 20.00 20.50
world:NN 20.00   7.48 20.00   0.00 18.00 15.00 12.84   9.60
NO_WORD   1.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.37 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : would
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "arisen" aligned badly to "been"
-1.00  1.00 Structure.relMismatch : text "Iraq" is nsubj of "been" while hyp "Iraq" is prep_from of "arisen" which aligned to text "been"
Hand-tuned score (dot product of above): 0.5787
Threshold: -1.8794


Inference ID: 1972

Txt: Rodriguez told detectives he never touched the burning backpack, which was loaded with plastic pipes packed with gunpowder and BBs.

Hyp: The burning backpack contained plastic pipes packed with gunpowder and BBs. (yes)

The
DT
burning
NN
backpack
NN
contained
VBD
plastic
JJ
pipes
NNS
packed
VBN
gunpowder
NN
BBs
NN
Rodriguez:NNP 20.50 10.50 10.50 15.50 12.50 10.50 15.50 10.50 10.50
told:VBD 20.00 14.92 13.13   8.55 12.00 15.00   7.15 15.00 15.00
detectives:NNS 20.00   8.13   9.21 11.75 10.36   9.13 13.96   8.02 10.00
he:PRP 18.00 12.00 12.00 15.00 15.00 12.00 15.00 12.00 12.00
never:RB 20.00 15.00 15.00 20.00 12.00 15.00 20.00 15.00 15.00
touched:VBD 20.00 14.24 15.00   9.29 12.00 15.00   9.27 14.59 15.00
the:DT   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
burning:VBG 20.00   0.00 13.05   8.09   8.11   9.15   6.17 11.20 15.00
backpack:NN 20.00   8.05   0.00 15.00   7.10   6.64 13.08   9.27 10.00
which:WDT 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
was:VBD 20.00 15.00 15.00 10.00 12.00 15.00 10.00 15.00 15.00
loaded:VBN 20.00 11.76 12.29   7.21   8.10 12.33   8.56 12.53 15.00
plastic:JJ 20.00   8.11   7.10   9.95   0.00   6.47   9.18   7.28 12.00
pipes:NNS 20.00   4.15   6.64 11.81   6.47   0.00 13.32   6.03 10.00
packed:VBN 20.00 11.17 13.08   8.94   9.18 13.32   0.00 13.26 15.00
gunpowder:NN 20.00   6.20   9.27 12.61   7.28   6.03 13.26   0.00 10.00
BBs:NN 20.00 10.00 10.00 15.00 12.00 10.00 15.00 10.00   0.00
NO_WORD   1.00 10.00 10.00 10.00   9.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.74 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "contained" aligned badly to "loaded"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "pipes" <-nsubjpass-- "packed" vs. hyp "pipes" <-dobj-- "contained", which aligned to text "loaded" args have different parents, different relations: text "backpack" <-dobj-- "touched" vs. hyp "backpack" <-nsubj-- "contained", which aligned to text "loaded" text "backpack" is nsubjpass of "loaded" while hyp "backpack" is nsubj of "contained" which aligned to text "loaded" text "pipes" is prep_with of "loaded" while hyp "pipes" is dobj of "contained" which aligned to text "loaded"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.3529
Threshold: -1.8794


Inference ID: 1436

Txt: Blair has sympathy for anyone who has lost their lives in Iraq.

Hyp: Blair is sorry for anyone who has lost their lives in Iraq. (yes)

Blair
NNP
is
VBZ
sorry
JJ
anyone
NN
who
WP
has
VBZ
lost
VBN
their
PRP$
lives
NNS
Iraq
NNP
Blair:NNP   0.00 15.50 12.50 10.50 12.50 15.50 15.50 12.50   9.64   9.88
has:VBZ 15.50   8.64 12.00 15.00 15.00   0.00 10.00 15.00 15.00 15.50
sympathy:NN 10.13 15.00   9.19 10.00 12.00 15.00 13.53 12.00   5.98 10.22
anyone:NN 10.50 15.00 12.00   0.00 12.00 15.00 15.00 12.00 10.00 10.50
who:WP 12.50 15.00 15.00 12.00   0.00 15.00 15.00 10.00 12.00 12.50
has:VBZ 15.50   8.64 12.00 15.00 15.00   0.00 10.00 15.00 15.00 15.50
lost:VBN 15.50 10.00 11.61 15.00 15.00 10.00   0.00 15.00 13.87 15.50
their:PRP$ 12.50 15.00 15.00 12.00 10.00 15.00 15.00   0.00 12.00 12.50
lives:NNS   9.64 15.00 11.03 10.00 12.00 15.00 13.87 12.00   0.00   9.79
Iraq:NNP   9.88 15.50 12.50 10.50 12.50 15.50 15.50 12.50   9.79   0.00
NO_WORD 10.00   1.00   9.00 10.00   1.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.57 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.functionWord : who
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "sorry" aligned badly to "sympathy"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Blair" <-nsubj-- "has" vs. hyp "Blair" <-nsubj-- "sorry", which aligned to text "sympathy"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -5.2742
Threshold: -1.8794


Inference ID: 1993

Txt: Profits nearly doubled to nearly $1.8 billion.

Hyp: Profits nearly doubled from nearly $1.8 billion. (don't know)

Profits
NNS
nearly
RB
doubled
VBD
nearly
RB
$
$
1.8
CD
billion
CD
Profits:NNS   0.00 15.00 15.00 15.00 20.50 20.50 20.50
nearly:RB 15.00   0.00 15.26   0.00 18.45 17.80 18.44
doubled:VBD 15.00 15.26   0.00 15.26 17.42 18.46 18.45
nearly:RB 15.00   0.00 15.26   0.00 18.45 17.80 18.44
$:$ 20.50 18.45 17.42 18.45   0.00 16.47 16.94
1.8:CD 20.50 17.80 18.46 17.80 16.47   0.00   0.00
billion:CD 20.50 18.44 18.45 18.44 16.94   0.00   0.00
NO_WORD 10.00   9.00 10.00   9.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.93 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.86 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added $[$-$]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "$" of "doubled" dropped on aligned hyp word "doubled"
-3.00  1.00 NullPunisher.entity : $
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.8104
Threshold: -1.8794


Inference ID: 1437

Txt: Iraq would have been an unsafe, unsecure country that did pose a threat to the world.

Hyp: Iraq would have constituted a threat to the world. (yes)

Iraq
NNP
would
MD
have
VB
constituted
VBD
a
DT
threat
NN
the
DT
world
NN
Iraq:NNP   0.00 20.50 15.50 15.50 20.50   9.81 20.50   9.60
would:MD 20.50   0.00 18.69 20.00 10.00 20.00 10.00 18.00
have:VB 15.50 18.69   0.00 10.00 20.00 15.00 20.00 15.00
been:VBN 15.50 20.00 10.00 10.00 20.00 15.00 20.00 15.00
an:DT 20.50 10.00 20.00 20.00   8.73 20.00 10.00 20.00
unsafe:JJ 12.50 20.00 12.00 11.36 20.00   9.89 20.00 11.16
unsecure:JJ 12.50 20.00 12.00 12.00 20.00 12.00 20.00 12.00
country:NN   5.33 20.00 15.00 14.00 20.00   6.97 20.00   5.47
that:WDT 20.50 10.00 20.00 20.00 10.00 18.00 10.00 20.00
did:VBD 15.50 18.57 10.00   9.55 20.00 14.42 20.00 14.97
pose:VB 15.50 20.00   8.51   9.99 20.00 10.09 20.00 14.47
a:DT 20.50 10.00 20.00 20.00   0.00 20.00 10.00 20.00
threat:NN   9.81 20.00 15.00 14.12 20.00   0.00 20.00   7.48
the:DT 20.50 10.00 20.00 20.00 10.00 20.00   0.00 20.00
world:NN   9.60 18.00 15.00 13.02 20.00   7.48 20.00   0.00
NO_WORD 10.00 10.00   1.00 10.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.35 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : would
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "constituted" aligned badly to "been"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "threat" <-dobj-- "pose" vs. hyp "threat" <-dobj-- "constituted", which aligned to text "been" args have different parents but same relations: text "world" <-prep_to-- "pose" vs. hyp "world" <-prep_to-- "constituted", which aligned to text "been"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.2407
Threshold: -1.8794


Inference ID: 2050

Txt: Five other soldiers have been ordered to face courts-martial.

Hyp: Five other soldiers have been refused to face courts-martial. (don't know)

Five
CD
other
JJ
soldiers
NNS
have
VBP
been
VBN
refused
VBN
to
TO
face
VB
courts-martial
NN
Five:CD   0.00 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
other:JJ 20.50   0.00 12.00 12.00 12.00 12.00 20.00 12.00 12.00
soldiers:NNS 20.50 12.00   0.00 15.00 15.00 12.64 20.00 14.51   9.82
have:VBP 20.50 12.00 15.00   0.00 10.00 10.00 20.00   7.33 15.00
been:VBN 20.50 12.00 15.00 10.00   0.00 10.00 20.00 10.00 15.00
ordered:VBN 20.50 12.00 12.98 10.00 10.00   4.31 20.00 10.00 12.31
to:TO 20.50 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00
face:VB 20.50 12.00 14.51   7.33 10.00   8.68 20.00   0.00 13.46
courts-martial:NN 20.50 12.00   9.82 15.00 15.00 13.64 20.00 13.46   0.00
NO_WORD 10.00   9.00 10.00   1.00   1.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.70 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.89 Alignment.txtSpan
-0.10  1.00 NullPunisher.functionWord : to
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "refused" aligned badly to "ordered"
Hand-tuned score (dot product of above): 1.1218
Threshold: -1.8794


Inference ID: 1439

Txt: The prosecutor told the court that the incident had caused "distress" to one of the children.

Hyp: The prosecutor told the court that "distress" in one of the children is associated with the incident. (yes)

The
DT
prosecutor
NN
told
VBD
the
DT
court
NN
that
IN
distress
NN
one
CD
the
DT
children
NNS
is
VBZ
associated
VBN
the
DT
incident
NN
The:DT   0.00 20.00 20.00   0.00 20.00 20.00 20.00 20.50   0.00 20.00 20.00 20.00   0.00 20.00
prosecutor:NN 20.00   0.00 13.22 20.00   4.12 20.00   8.58 20.50 20.00   7.82 15.00 14.75 20.00   5.09
told:VBD 20.00 13.22   0.00 20.00 14.78 20.00 15.00 20.50 20.00 14.65 10.00   7.96 20.00 11.47
the:DT   0.00 20.00 20.00   0.00 20.00 20.00 20.00 20.50   0.00 20.00 20.00 20.00   0.00 20.00
court:NN 20.00   4.12 14.78 20.00   0.00 20.00   8.86 20.50 20.00   8.20 15.00 15.00 20.00   8.52
that:IN 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00
the:DT   0.00 20.00 20.00   0.00 20.00 20.00 20.00 20.50   0.00 20.00 20.00 20.00   0.00 20.00
incident:NN 20.00   5.09 11.47 20.00   8.52 20.00   8.05 20.50 20.00   8.75 15.00 12.67 20.00   0.00
had:VBD 20.00 15.00 10.00 20.00 15.00 20.00 15.00 20.50 20.00 15.00 10.00 10.00 20.00 15.00
caused:VBN 20.00 13.96 10.00 20.00 15.00 20.00 10.04 20.50 20.00 15.00 10.00   6.01 20.00 11.29
distress:NN 20.00   8.58 15.00 20.00   8.86 20.00   0.00 20.50 20.00   8.97 15.00 11.43 20.00   8.05
one:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50   0.00 20.50 20.50 20.50 20.50 20.50 20.50
the:DT   0.00 20.00 20.00   0.00 20.00 20.00 20.00 20.50   0.00 20.00 20.00 20.00   0.00 20.00
children:NNS 20.00   7.82 14.65 20.00   8.20 20.00   8.97 20.50 20.00   0.00 15.00 15.00 20.00   8.75
NO_WORD   1.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00   1.00 10.00   1.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.44 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  10.00 Alignment.hypSpan
 0.10  0.71 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
Hand-tuned score (dot product of above): 3.2817
Threshold: -1.8794


Inference ID: 1976

Txt: Bomb-sniffing dogs were brought to Rodriguez's Mulberry St. apartment.

Hyp: Bomb-sniffing dogs were estimated at Rodriguez's Mulberry St. apartment. (don't know)

Bomb-sniffing
JJ
dogs
NNS
were
VBD
estimated
VBN
Rodriguez
NNP
Mulberry
NNP
St.
NNP
apartment
NN
Bomb-sniffing:JJ   0.00   9.88 12.00 11.77 12.50 12.00 12.00 11.15
dogs:NNS   9.88   0.00 15.00 15.00 10.50 10.00 10.00   5.95
were:VBD 12.00 15.00   0.00 10.00 15.50 15.00 15.00 15.00
brought:VBN 10.89 15.00 10.00   8.15 15.50 15.00 15.00 12.96
Rodriguez:NNP 12.50 10.50 15.50 15.50   0.50 10.50 10.50 10.50
Mulberry_St.:NNP 12.50 10.50 15.50 15.50 10.50   5.50 10.50 10.50
apartment:NN 11.15   5.95 15.00 15.00 10.50 10.00 10.00   0.00
NO_WORD   9.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.60 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.88 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added St.[St.-NNP]
-1.00  1.00 NullPunisher.other : St.
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "estimated" aligned badly to "brought"
-1.00  1.00 Structure.relMismatch : text "apartment" is prep_to of "brought" while hyp "apartment" is prep_at of "estimated" which aligned to text "brought"
Hand-tuned score (dot product of above): -1.8861
Threshold: -1.8794


Inference ID: 1965

Txt: Rich and poor nations struck a historic deal on Sunday to slash billions of dollars in farm subsidies, create more open industrial markets and revive stalled world trade talks that could boost global growth.

Hyp: Rich and poor nations crashed into a historic deal on Sunday. (don't know)

Rich
NNP
poor
JJ
nations
NNS
crashed
VBD
a
DT
historic
JJ
deal
NN
Sunday
NNP
Rich:NNP   0.00 12.00   8.61 15.00 20.00 12.00   8.86   9.77
poor:JJ 12.00   0.00 11.15 11.25 20.00 10.00 12.00 12.50
nations:NNS   8.61 11.15   0.00 15.00 20.00 12.00   7.25   8.47
struck:VBD 15.00 12.00 15.00   6.82 20.00 11.41 15.00 15.50
a:DT 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
historic:JJ 12.00 10.00 12.00 10.00 20.00   0.00 11.35 12.50
deal:NN   8.86 12.00   7.25 15.00 20.00 11.35   0.00   8.80
Sunday:NNP   9.77 12.50   8.47 15.50 20.50 12.50   8.80   0.00
to:TO 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.50
slash:VB 15.00 12.00 15.00 10.00 20.00 12.00 13.76 15.50
billions:NNS   9.84 12.50   8.59 13.61 20.50 11.75   8.90   7.98
dollars:NNS   9.44 12.50   7.88 15.50 20.50 11.61   8.27   7.37
farm:NN   9.25 10.13   7.94 13.97 20.00 10.05   8.15   9.30
subsidies:NNS   9.44 10.07   8.29 14.62 20.00 12.00   8.59   9.56
create:VB 15.00 10.81 14.49 10.00 20.00 12.00 12.33 15.50
more:RBR 15.00 12.00 15.00 20.00 20.00 12.00 15.00 15.50
open:JJ 12.00 10.00 12.00 10.10 20.00   8.36 12.00 12.50
industrial:JJ 12.00   4.63 11.38 10.76 20.00   9.12 12.00 12.50
markets:NNS   8.51 10.66   6.67 15.00 20.00 12.00   5.86   8.36
revive:VB 15.00 12.00 15.00   9.48 20.00   8.93 13.98 15.50
stalled:VBN 15.00 11.98 14.83   8.93 20.00 10.27 12.64 15.50
world:NN   8.92 10.57   5.96 13.77 20.00 10.69   7.73   8.87
trade:NN   8.98 12.00   6.84 14.74 20.00 12.00   4.66   8.95
talks:NNS   9.06 12.00   7.01 14.78 20.00 12.00   5.09   8.44
that:WDT 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.50
could:MD 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.50
boost:VB 15.00 12.00 15.00 10.00 20.00 10.57 14.95 15.50
global:JJ 12.00 10.00   8.65 12.00 20.00 10.00 11.98 12.50
growth:NN   8.87 12.00   7.25 15.00 20.00 10.11   7.65   8.80
NO_WORD 10.00   9.00 10.00 10.00   1.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.73 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  8.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1000
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "crashed" aligned badly to "struck"
-1.00  1.00 Structure.relMismatch : text "deal" is dobj of "struck" while hyp "deal" is prep_into of "crashed" which aligned to text "struck"
Hand-tuned score (dot product of above): 2.0716
Threshold: -1.8794


Inference ID: 1966

Txt: Relatives of a missing woman and her husband said late Saturday that new information from the husband has prompted them to call off a volunteer search for the woman.

Hyp: Relatives of a missing woman and her husband cancelled a volunteer search for the woman. (yes)

Relatives
NNS
a
DT
missing
JJ
woman
NN
her
PRP$
husband
NN
canceled
VBD
a
DT
volunteer
NN
search
NN
the
DT
woman
NN
Relatives:NNS   0.00 20.00 12.00   5.46 12.00   3.15 15.00 20.00   7.56   7.74 20.00   5.46
a:DT 20.00   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 10.00 20.00
missing:JJ 12.00 20.00   0.00 10.02 15.00 10.78 10.27 20.00 10.07   8.15 20.00 10.02
woman:NN   5.46 20.00 10.02   0.00 12.00   3.59 15.00 20.00   7.93   8.09 20.00   0.00
her:PRP$ 12.00 20.00 15.00 12.00   0.00 12.00 15.00 20.00 12.00 12.00 20.00 12.00
husband:NN   3.15 20.00 10.78   3.59 12.00   0.00 14.18 20.00   8.38   9.02 20.00   3.59
said:VBD 15.00 20.00 12.00 15.00 15.00 15.00 10.00 20.00 14.52 13.53 20.00 15.00
late:JJ 12.50 20.50   9.85 11.91 15.50 11.82 10.01 20.50 11.41 11.66 20.50 11.91
Saturday:NNP   8.63 20.50 12.50   8.94 12.50   9.75 15.50 20.50   9.94   9.45 20.50   8.94
that:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
new:JJ 12.00 20.00   9.33 12.00 15.00 12.00 10.81 20.00 12.00 11.82 20.00 12.00
information:NN   6.17 20.00   9.88   6.66 12.00   8.02 14.83 20.00   8.36   4.74 20.00   6.66
the:DT 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00   0.00 20.00
husband:NN   3.15 20.00 10.78   3.59 12.00   0.00 14.18 20.00   8.38   9.02 20.00   3.59
has:VBZ 15.00 20.00 12.00 15.00 15.00 15.00 10.00 20.00 15.00 15.00 20.00 15.00
prompted:VBN 15.00 20.00 10.89 14.74 15.00 15.00   6.49 20.00 15.00 12.72 20.00 14.74
them:PRP 12.00 20.00 15.00 12.00   7.78 12.00 15.00 20.00 12.00 12.00 15.71 12.00
to:TO 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00
call_off:VB 15.00 20.00 10.84 15.00 15.00 15.00 10.00 20.00 13.55 12.57 20.00 15.00
a:DT 20.00   0.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 10.00 20.00
volunteer:NN   7.56 20.00 10.07   7.93 12.00   8.38 14.26 20.00   0.00   7.78 20.00   7.93
search:NN   7.74 20.00   8.15   8.09 12.00   9.02 14.47 20.00   7.78   0.00 20.00   8.09
the:DT 20.00 10.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00   0.00 20.00
woman:NN   5.46 20.00 10.02   0.00 12.00   3.59 15.00 20.00   7.93   8.09 20.00   0.00
NO_WORD 10.00   1.00   9.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.83 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  12.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: said-VBD
 1.00  1.00 Quant.contract : [a,a]
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "canceled" aligned badly to "call_off"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Relatives" <-nsubj-- "said" vs. hyp "Relatives" <-nsubj-- "canceled", which aligned to text "call_off" args have different parents but same relations: text "woman" <-prep_for-- "search" vs. hyp "woman" <-prep_for-- "canceled", which aligned to text "call_off"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -1.4808
Threshold: -1.8794


Inference ID: 1959

Txt: Kerry hit Bush hard on his conduct on the war in Iraq.

Hyp: Kerry shot Bush. (don't know)

Kerry
NNP
shot
VBD
Bush
NNP
Kerry:NNP   0.50 15.00   9.46
hit:VBD 15.50   2.78 15.50
Bush:NNP   8.96 15.50   0.00
hard:JJ 12.50 10.78 12.50
his:PRP$ 12.50 15.00 12.50
conduct:NN 10.50 15.00   8.57
the:DT 20.50 20.00 20.50
war:NN 10.50 13.93   8.65
Iraq:NNP 10.50 15.50   9.30
NO_WORD 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.02 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : shot
-2.00  1.00 RootEntailment.unalignedRoot : "shot" not aligned to anything
Hand-tuned score (dot product of above): -1.0323
Threshold: -1.8794


Inference ID: 1975

Txt: A matching fingerprint was discovered on the strap.

Hyp: A matching fingerprint was excavated from the strap. (don't know)

A
DT
matching
JJ
fingerprint
NN
was
VBD
excavated
VBN
the
DT
strap
NN
A:DT   0.00 20.00 20.00 20.00 20.00 10.00 20.00
matching:JJ 20.00   0.00 11.50 12.00 11.88 20.00 10.24
fingerprint:NN 20.00 11.50   0.00 15.00 13.06 20.00   8.45
was:VBD 20.00 12.00 15.00   0.00 10.00 20.00 15.00
discovered:VBN 20.00 11.87 11.25 10.00   6.71 20.00 15.00
the:DT 10.00 20.00 20.00 20.00 20.00   0.00 20.00
strap:NN 20.00 10.24   8.45 15.00 13.82 20.00   0.00
NO_WORD   1.00   9.00 10.00   1.00 10.00   1.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.47 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "excavated" aligned badly to "discovered"
-1.00  1.00 Structure.relMismatch : text "strap" is prep_on of "discovered" while hyp "strap" is prep_from of "excavated" which aligned to text "discovered"
Hand-tuned score (dot product of above): 1.1879
Threshold: -1.8794


Inference ID: 34

Txt: Saudi Arabia, the biggest oil producer in the world, was once a supporter of Osama bin Laden and his associates who led attacks against the United States.

Hyp: Saudi Arabia is a key U.S. ally. (don't know)

Saudi_Arabia
NNP
is
VBZ
a
DT
key
JJ
U.S.
NNP
ally
NN
Saudi_Arabia:NNP   0.00 15.50 20.50 12.50 10.00 10.50
the:DT 20.50 20.00 10.00 20.00 20.50 20.00
biggest:JJS 12.50 12.00 20.00   9.30 12.50 10.84
oil:NN 10.50 15.00 20.00 12.00   7.96   8.93
producer:NN 10.50 15.00 20.00 12.00   7.94   8.92
the:DT 20.50 20.00 10.00 20.00 20.50 20.00
world:NN 10.50 15.00 20.00 11.69   6.53   7.99
was:VBD 15.50   0.00 20.00 12.00 15.50 15.00
once:RB 15.50 20.00 20.00 12.00 15.50 15.00
a:DT 20.50 20.00   0.00 20.00 20.50 20.00
supporter:NN 10.50 15.00 20.00 11.08   8.18   3.72
Osama_bin_Laden:NNP 10.50 15.50 20.50 12.50 10.50 10.50
his:PRP$ 12.50 13.00 20.00 15.00 12.50 12.00
associates:NNS 10.50 15.00 20.00 10.83   7.41   8.59
who:WP 12.50 15.00 20.00 15.00 12.50 12.00
led:VBD 15.50 10.00 20.00 10.56 15.50 14.18
attacks:NNS 10.50 15.00 20.00 10.86   7.86   7.31
the:DT 20.50 20.00 10.00 20.00 20.50 20.00
United_States:NNP 10.00 15.50 20.50 12.50   0.00   9.00
NO_WORD 10.00   1.00   1.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.76 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added key[key-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Osama_bin_Laden" of "supporter" dropped on aligned hyp word "ally"
-1.00  1.00 NullPunisher.other : key
-0.05  1.00 NullPunisher.aux : is
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "United_States" <-prep_against-- "led" vs. hyp "U.S." <-nn-- "ally", which aligned to text "supporter"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.4052
Threshold: -1.8794


Inference ID: 1985

Txt: Quebec woman and her mother accused of plotting to kill a four-year-old girl.

Hyp: Quebec woman murdered a four-year-old girl. (don't know)

Quebec
NNP
woman
NN
murdered
VBN
a
DT
four-year-old
JJ
girl
NN
Quebec:NNP   0.00   8.30 15.00 20.00 12.00   8.93
woman:NN   8.30   0.00   9.19 20.00 12.00   1.68
her:PRP$ 12.00 12.00 15.00 20.00 15.00 12.00
mother:NN   8.96   3.52 11.10 20.00 11.41   1.76
accused:VBD 15.00 12.20   4.06 20.00 12.00 13.25
of:IN 20.00 20.00 20.00 20.00 20.00 20.00
plotting:VBG 15.00 14.66   6.71 20.00 12.00 15.00
to:TO 20.00 20.00 20.00 10.00 20.00 20.00
kill:VB 15.00 11.06   4.04 20.00 12.00 11.20
a:DT 20.00 20.00 20.00   0.00 20.00 20.00
four-year-old:JJ 12.00 12.00 11.75 20.00   0.00 12.00
girl:NN   8.93   1.68 10.41 20.00 12.00   0.00
NO_WORD 10.00 10.00 10.00   1.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.71 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: kill -> murder
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "murdered" aligned badly to "kill"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "woman" <-nsubj-- "accused" vs. hyp "woman" <-nsubj-- "murdered", which aligned to text "kill"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.6852
Threshold: -1.8794


Inference ID: 1971

Txt: Rodriguez was spotted by a witness running away from the stairwell where the backpack was found seconds before it went off.

Hyp: A witness rescued Rodriguez. (don't know)

A
DT
witness
NN
rescued
VBD
Rodriguez
NNP
Rodriguez:NNP 20.50 10.50 15.50   0.00
was:VBD 20.00 15.00 10.00 15.50
spotted:VBN 20.00 11.84   7.35 15.50
a:DT   0.00 20.00 20.00 20.50
witness:NN 20.00   0.00 13.60 10.50
running_away:VBG 20.00 15.00 10.00 15.50
the:DT 10.00 20.00 20.00 20.50
stairwell:NN 20.00   9.20 12.13 10.50
where:WRB 10.00 20.00 20.00 20.50
the:DT 10.00 20.00 20.00 20.50
backpack:NN 20.00   9.16 14.21 10.50
was:VBD 20.00 15.00 10.00 15.50
found:VBN 20.00 11.24   6.90 15.50
seconds:NNS 20.00   8.82 13.65 10.50
before:IN 20.00 20.00 20.00 20.50
it:PRP 20.00 12.00 15.00 12.50
went_off:VBD 20.00 15.00 10.00 15.50
NO_WORD   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.27 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "running_away" of "witness" dropped on aligned hyp word "witness"
-1.00  1.00 NullPunisher.other : rescued
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "rescued" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.3258
Threshold: -1.8794


Inference ID: 2064

Txt: The Osaka World Trade Center is the tallest building in Western Japan.

Hyp: The Osaka World Trade Center is the tallest building in Japan. (don't know)

The
DT
Osaka_World_Trade_Center
NNP
is
VBZ
the
DT
tallest
JJS
building
NN
Japan
NNP
The:DT   0.00 20.50 20.00   0.00 20.00 20.00 20.50
Osaka_World_Trade_Center:NNP 20.50   0.00 15.50 20.50 12.50   8.86   9.39
is:VBZ 20.00 15.50   0.00 20.00 12.00 15.00 15.50
the:DT   0.00 20.50 20.00   0.00 20.00 20.00 20.50
tallest:JJS 20.00 12.50 12.00 20.00   0.00   6.69 12.50
building:NN 20.00   8.86 15.00 20.00   6.69   0.00   7.06
Western_Japan:NNP 20.50   9.97 15.50 20.50 12.50   8.44   5.00
NO_WORD   1.00 10.00   1.00   1.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.64 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.00  1.00 NegPolarity.hypNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord&NegPolarity.hypNegWord :
Hand-tuned score (dot product of above): 3.3691
Threshold: -1.8794


Inference ID: 1490

Txt: "It's Only a Play" is Terrence McNally's "Give My Regards to Broadway," a lightweight tribute to the theater as seen from the lofty but limited vantage point of Broadway artists who are consumed by New York's hit/flop mentality.

Hyp: Broadway is in New York. (yes)

Broadway
NNP
is
VBZ
New_York
NNP
It:PRP 12.00 15.00 12.50
's:VBZ 15.00   0.00 15.50
Only:RB 15.00 20.00 15.50
a:DT 20.00 20.00 20.50
Play:NN   9.53 15.00 10.50
is:VBZ 15.00   0.00 15.50
Terrence_McNally:NNP 10.50 15.50 10.50
's:VBZ 15.00   0.00 15.50
Give:NNP   9.44 15.00 10.50
My:PRP$ 12.00 15.00 12.50
Regards:NNS   9.65 15.00 10.50
Broadway:NNP   0.50 15.50 10.00
a:DT 20.00 20.00 20.50
lightweight:JJ 12.00 12.00 12.50
tribute:NN   9.81 15.00 10.50
the:DT 20.00 20.00 20.50
theater:NN   8.22 15.00 10.50
as:RB 15.00 20.00 15.50
seen:VBN 15.00 10.00 15.50
the:DT 20.00 20.00 20.50
lofty:JJ 12.00 12.00 12.50
limited:JJ 12.00 12.00 12.50
vantage_point:NN   9.39 15.00 10.50
Broadway:NNP   0.00 15.00 10.50
artists:NNS   8.31 15.00 10.50
who:WP 12.00 15.00 12.50
are:VBP 15.00   0.00 15.50
consumed:VBN 15.00 10.00 15.50
New_York:NNP 10.50 15.50   0.00
hit\/flop:NN   9.39 15.00 10.50
mentality:NN   9.53 15.00 10.50
NO_WORD 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.32 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-2.00  1.00 Location.mismatch : no clear info of matching: be(X, prep_in)
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "New_York" <-poss-- "mentality" vs. hyp "New_York" <-prep_in-- "is", which aligned to text "is" args have different parents, different relations: text "Broadway" <-prep_of-- "vantage_point" vs. hyp "Broadway" <-nsubj-- "is", which aligned to text "is"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -5.4353
Threshold: -1.8794


Inference ID: 647

Txt: Macedonian Milcho Manchevski's "Before the Rain" and Taiwanese Tsai Ming-liang's "Aiqing Wansui/Vive L'Amour" shared the Golden Lion award for best film.

Hyp: "Before the Rain" and "Vive L'Amour" won the Golden Lion. (yes)

Before
IN
the
DT
Rain
NN
``
``
Vive
NNP
L'Amour
NNP
won
VBD
the
DT
Golden
NNP
Lion
NNP
Macedonian_Milcho_Manchevski:NNP 20.50 20.50 10.50 20.50 10.50 10.50 15.50 20.50 10.50 10.50
Before:NNP   0.00 20.00   9.73 20.00 10.00 10.00 15.00 20.00   9.48   9.78
the:NNP 20.00   0.00 10.00 20.00 10.00 10.00 15.00   0.00 10.00 10.00
Rain:NNP 20.00 20.00   0.00 20.00 10.00 10.00 15.00 20.00   9.10   9.54
Taiwanese_Tsai_Ming-liang:NNP 20.50 20.50 10.27 20.40 10.50 10.50 15.35 20.50 10.04   9.21
Aiqing:NNP 20.00 20.00 10.00 20.00 10.00 10.00 15.00 20.00 10.00 10.00
Wansui\/Vive:NNP 20.00 20.00 10.00 20.00   5.00 10.00 15.00 20.00 10.00 10.00
L'Amour:NNP 20.00 20.00 10.00 20.00 10.00   0.00 15.00 20.00 10.00 10.00
shared:VBD 20.00 20.00 15.00 20.00 15.00 15.00   7.83 20.00 15.00 15.00
the:DT 20.00   0.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00 20.00
Golden_Lion:NNP 20.50 20.50 10.04 20.50 10.50 10.50 15.50 20.50   5.50   0.50
award:NN 20.00 20.00   9.40 20.00 10.00 10.00 10.54 20.00   9.01   9.48
best:JJS 20.00 20.00 12.00 17.66 12.00 12.00   8.13 20.00 12.00 12.00
film:NN 20.00 20.00   8.88 18.79 10.00 10.00 15.00 20.00   8.34   9.01
NO_WORD 10.00   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.15 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "film" of "shared" dropped on aligned hyp word "won"
-0.10  1.00 NullPunisher.functionWord : ``
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "won" aligned badly to "shared"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Before" <-dep-- "Rain" vs. hyp "Before" <-advmod-- "won", which aligned to text "shared"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.4992
Threshold: -1.8794


Inference ID: 2086

Txt: Jigish Avalani, group manager of Windows DNA marketing at Microsoft, explains that Windows DNA refers to the Windows Distributed interNet Application architecture, launched by Microsoft in fall of 1997.

Hyp: Microsoft was established in fall of 1997. (don't know)

Microsoft
NNP
was
VBD
established
VBN
fall
NN
1997
CD
Jigish_Avalani:NNP 10.50 15.50 15.50 10.50 20.50
group:NN   9.53 15.00 12.44   6.54 20.26
manager:NN 10.50 15.00 14.16   8.29 20.50
Windows_DNA:NNP 10.00 15.50 15.50   9.60 20.50
marketing:NN 10.50 15.00 13.00   8.25 20.50
Microsoft:NNP   0.00 15.50 15.50 10.50 20.50
explains:VBZ 15.50 10.00   9.39 15.00 20.50
that:IN 20.50 20.00 20.00 20.00 20.50
Windows:NNP 10.50 15.00 15.00   9.10 20.50
DNA:NNP 10.50 15.00 15.00   9.40 20.50
refers:VBZ 15.50 10.00   9.04 14.60 20.34
the:DT 20.50 20.00 20.00 20.00 20.50
Windows:NNP 10.50 15.00 15.00   9.10 20.50
Distributed:NNP 10.50 15.00 15.00   8.20 20.50
interNet:NNP 10.50 15.00 15.00   8.72 20.50
Application:NNP 10.50 15.00 15.00   8.56 20.50
architecture:NN 10.50 15.00 12.96   9.31 20.11
launched:VBN 15.50 10.00   5.05 14.89 20.49
Microsoft:NNP   0.00 15.50 15.50 10.50 20.50
fall:NN 10.50 15.00 15.00   0.00 16.36
1997:CD 20.50 20.50 20.50 16.36   0.00
NO_WORD 10.00   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.40 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1997
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : established
-2.00  1.00 RootEntailment.unalignedRoot : "established" not aligned to anything
Hand-tuned score (dot product of above): -0.1772
Threshold: -1.8794


Inference ID: 598

Txt: After the 1979 Soviet invasion and occupation, 3 million Afghans fled to Pakistan, which was encouraged by hefty Western aid to take them in.

Hyp: Afghanistan was invaded by the Soviet Union in 1979. (yes)

Afghanistan
NNP
was
VBD
invaded
VBN
the
DT
Soviet_Union
NNP
1979
CD
the:DT 20.50 20.00 20.00   0.00 20.50 20.50
1979:CD 20.50 20.50 16.73 20.50 20.50   0.00
Soviet:JJ 12.50 12.00 12.00 20.00   7.50 20.50
invasion:NN 10.26 15.00   2.50 20.00 10.22 18.08
occupation:NN   8.73 15.00 11.12 20.00   8.58 17.61
3:CD 20.50 20.50 20.50 20.50 20.50   9.55
million:CD 20.50 20.50 20.50 20.50 20.50 10.50
Afghans:NNPS   5.50 15.00 15.00 20.00   9.88 20.50
fled:VBD 15.50 10.00   5.62 20.00 15.50 17.89
Pakistan:NNP   4.37 15.50 15.50 20.50   6.14 20.50
which:WDT 20.50 20.00 20.00 10.00 20.50 20.50
was:VBD 15.50   0.00 10.00 20.00 15.50 20.50
encouraged:VBN 15.50 10.00   9.43 20.00 15.50 19.91
hefty:JJ 12.50 12.00 12.00 20.00 12.50 19.95
Western:JJ 12.50 12.00 12.00 20.00 12.50 20.50
aid:NN   9.67 15.00 13.78 20.00   9.58 19.73
to:TO 20.50 20.00 20.00 10.00 20.50 20.50
take:VB 15.50 10.00   9.42 20.00 15.50 20.50
them:PRP 12.50 15.00 15.00 15.71 12.50 20.50
in:RP 20.50 20.00 20.00 10.00 20.50 20.50
NO_WORD 10.00   1.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.56 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "3" of "Afghans" dropped on aligned hyp word "Afghanistan"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1979
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : invaded
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.unalignedRoot : "invaded" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.3288
Threshold: -1.8794


Inference ID: 2067

Txt: Andreessen, who helped define the Internet revolution as part of team that created the first Internet browser (Mosaic) and his co-founding Netscape, told a packed hall at the San Francisco Marriott hotel Thursday that he is "extremely committed" to his startup Loudcloud.

Hyp: The Internet browser Mosaic was created at the San Francisco Marriott hotel. (don't know)

The
DT
Internet
NNP
browser
FW
Mosaic
NNP
was
VBD
created
VBN
the
DT
San_Francisco_Marriott
NNP
hotel
NN
Andreessen:NNP 20.50 10.50 20.50 10.00 15.50 15.50 20.50 10.50 10.50
who:WP 20.00 12.00 20.00 12.50 15.00 15.00 20.00 12.50 12.00
helped:VBD 20.00 15.00 20.00 15.50 10.00   8.29 20.00 15.50 15.00
define:VB 20.00 15.00 18.82 15.50 10.00   7.31 20.00 15.50 15.00
the:DT   0.00 20.00 10.00 20.50 20.00 20.00   0.00 20.50 20.00
Internet:NN 20.00   0.00 20.00 10.50 15.00 15.00 20.00 10.50   7.00
revolution:NN 20.00   9.06 18.09 10.50 15.00 11.81 20.00 10.50   8.77
part:NN 20.00   7.39 20.00 10.50 15.00 13.95 20.00 10.50   6.87
team:NN 20.00   8.19 20.00 10.50 15.00 15.00 20.00 10.50   7.77
that:WDT 10.00 20.00 10.00 20.50 20.00 20.00 10.00 20.50 20.00
created:VBD 20.00 15.00 18.52 15.50 10.00   0.00 20.00 15.50 15.00
the:DT   0.00 20.00 10.00 20.50 20.00 20.00   0.00 20.50 20.00
first:JJ 20.00 12.00 20.00 12.50 12.00 12.00 20.00 12.50 12.00
Internet:NN 20.00   0.00 20.00 10.50 15.00 15.00 20.00 10.50   7.00
browser:NN 20.00   8.90   0.00 10.50 15.00 13.52 20.00 10.50 10.00
Mosaic:NNP 20.00 10.00 20.00   0.50 15.00 15.00 20.00 10.50 10.00
his:PRP$ 20.00 12.00 20.00 12.50 15.00 15.00 20.00 12.50 12.00
co-founding:JJ 20.00 12.00 20.00 12.50 12.00 12.00 20.00 12.50 12.00
Netscape:NNP 20.50 10.50 20.50 10.50 15.50 15.50 20.50 10.00 10.50
told:VBD 20.00 15.00 20.00 15.50 10.00 10.00 20.00 15.50 13.00
a:DT 10.00 20.00 10.00 20.50 20.00 20.00 10.00 20.50 20.00
packed:JJ 20.00 12.00 18.95 12.50 12.00 11.98 20.00 12.50   9.17
hall:NN 20.00   8.01 20.00 10.50 15.00 14.41 20.00 10.50   5.73
the:DT   0.00 20.00 10.00 20.50 20.00 20.00   0.00 20.50 20.00
San_Francisco_Marriott:NNP 20.50 10.50 20.50 10.50 15.50 15.50 20.50   0.00 10.50
hotel:NN 20.00   7.00 20.00 10.50 15.00 15.00 20.00 10.50   0.00
Thursday:NNP 20.50   9.16 20.50 10.50 15.50 15.50 20.50 10.50   8.80
that:IN 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.50 20.00
his:PRP$ 20.00 12.00 20.00 12.50 15.00 15.00 20.00 12.50 12.00
is:VBZ 20.00 15.00 20.00 15.50   0.00 10.00 20.00 15.50 15.00
extremely:RB 20.00 15.00 20.00 15.50 20.00 20.00 20.00 15.50 15.00
committed:JJ 20.00 12.00 20.00 12.50 12.00   9.85 20.00 12.50 12.00
his:PRP$ 20.00 12.00 20.00 12.50 15.00 15.00 20.00 12.50 12.00
startup:NNP 20.00   9.17 15.22 10.50 15.00 12.40 20.00 10.50   7.95
Loudcloud:NNP 20.50 10.50 20.50 10.00 15.50 15.50 20.50 10.50 10.50
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.69 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Internet" of "browser" dropped on aligned hyp word "browser"
-0.10  1.00 NullPunisher.article : The
-0.05  1.00 NullPunisher.aux : was
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "hotel" <-prep_at-- "told" vs. hyp "hotel" <-prep_at-- "created", which aligned to text "created" args have different parents, different relations: text "Mosaic" <-abbrev-- "browser" vs. hyp "Mosaic" <-nsubjpass-- "created", which aligned to text "created"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.2871
Threshold: -1.8794


Inference ID: 2084

Txt: Microsoft Israel was founded in 1989 and became one of the first Microsoft branches outside the USA.

Hyp: Microsoft was established in 1989. (don't know)

Microsoft
NNP
was
VBD
established
VBN
1989
CD
Microsoft:NNP   0.00 15.50 15.50 20.50
Israel:NNP   9.48 15.50 15.50 20.50
was:VBD 15.50   0.00 10.00 20.50
founded:VBN 15.50 10.00   4.28 15.30
1989:CD 20.50 20.50 16.21   0.00
became:VBD 15.50 10.00 10.00 20.50
one:CD 20.50 20.50 20.50 10.50
the:DT 20.50 20.00 20.00 20.50
first:JJ 12.50 12.00 12.00 20.50
Microsoft:NNP   0.00 15.50 15.50 20.50
branches:NNS 10.50 15.00 14.69 20.50
the:DT 20.50 20.00 20.00 20.50
USA:NNP 10.00 15.50 15.50 20.50
NO_WORD 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.31 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1989
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "established" aligned badly to "founded"
Hand-tuned score (dot product of above): 1.2163
Threshold: -1.8794


Inference ID: 1494

Txt: The demands on Italy's 53rd postwar government are apparent, as are the resentment and uncertainties Berlusconi has roused at the head of a new party with the catchy name Forza Italia (Go, Italy).

Hyp: Silvio Berlusconi is in Forza Italia. (yes)

Silvio_Berlusconi
NNP
is
VBZ
Forza_Italia
NNP
The:DT 20.50 20.00 20.50
demands:NNS 10.50 15.00   9.15
Italy:NNP 10.50 15.50   0.00
53rd:CD 20.50 20.50 20.50
postwar:JJ 12.50 12.00 12.50
government:NN 10.50 15.00   8.46
are:VBP 15.50   0.00 15.50
apparent:JJ 12.50 12.00 12.50
as:IN 20.50 20.00 20.50
are:VBP 15.50   0.00 15.50
the:DT 20.50 20.00 20.50
resentment:NN 10.50 15.00   9.73
uncertainties:NNS 10.50 15.00   9.24
Berlusconi:NNP   5.00 15.50 10.50
has:VBZ 15.50   8.64 15.50
roused:VBN 15.50 10.00 15.50
the:DT 20.50 20.00 20.50
head:NN 10.50 15.00   9.04
a:DT 20.50 20.00 20.50
new:JJ 12.50 12.00 12.50
party:NN 10.50 15.00   9.10
the:DT 20.50 20.00 20.50
catchy:JJ 12.50 12.00 12.50
name:NN 10.50 15.00   9.17
Forza_Italia:NNP 10.00 15.50   0.50
Go:NNP 10.50 15.00   9.64
Italy:NNP 10.50 15.50   0.00
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.60 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Italy" of "Forza_Italia" dropped on aligned hyp word "Forza_Italia"
-2.00  1.00 Location.mismatch : no clear info of matching: be(X, prep_in)
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "is" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.5820
Threshold: -1.8794


Inference ID: 637

Txt: In 1969, more than 500 million people around the world sat in front of television sets, watching grainy images of two men in white bulky spacesuits planting a U.S. flag on the lunar landscape with its black horizon.

Hyp: The Apollo astronauts waved the American flag on the moon in 1969. (yes)

The
DT
Apollo
NNP
astronauts
NNS
waved
VBD
the
DT
American_flag
NN
the
DT
moon
NNP
1969
CD
1969:CD 20.50 20.50 19.79 20.15 20.50 18.32 20.50 19.61   0.00
more_than:IN 20.00 20.00 20.00 20.00 20.00 20.50 20.00 20.50 20.50
500:CD 20.50 20.50 19.93 20.05 20.50 20.50 20.50 20.50   6.66
million:CD 20.50 20.50 20.49 20.50 20.50 20.21 20.50 20.50 10.50
people:NNS 20.00   8.73   7.80 15.00 20.00 10.50 20.00   8.44 20.45
the:DT   0.00 20.00 20.00 20.00   0.00 20.50   0.00 20.50 20.50
world:NN 20.00   9.34   8.68 15.00 20.00   8.85 20.00   9.28 19.47
sat:VBD 20.00 15.00 13.86   4.01 20.00 13.16 20.00 12.92 19.70
front:NN 20.00   9.63   8.85   8.67 20.00   5.97 20.00   8.39 20.19
television_sets:NNS 20.00   9.43   8.35 15.00 20.00 10.50 20.00   8.96 20.50
watching:VBG 20.00 15.00 14.79   9.04 20.00 15.50 20.00 15.42 20.50
grainy:JJ 20.00 12.00 11.15   9.29 20.00 10.82 20.00 11.56 19.56
images:NNS 20.00   7.63   7.65 15.00 20.00 10.30 20.00   8.12 19.86
two:CD 20.50 20.50 19.59 20.50 20.50 20.50 20.50 20.26 10.50
men:NNS 20.00   8.95   8.12 13.16 20.00   9.39 20.00   8.74 20.50
white:JJ 20.00 12.00 12.00 10.73 20.00 10.51 20.00 12.50 20.50
bulky:JJ 20.00 12.00 11.05 10.88 20.00 11.03 20.00 11.68 20.50
spacesuits:NNS 20.00 10.00   5.23 15.00 20.00 10.50 20.00 10.50 20.50
planting:VBG 20.00 15.00 13.34 10.00 20.00 15.50 20.00 13.78 20.50
a:DT 10.00 20.00 20.00 20.00 10.00 20.50 10.00 20.50 20.50
U.S.:NNP 20.50   9.63   8.87 15.50 20.50 10.00 20.50   8.49 20.50
flag:NN 20.00   9.47   8.42 10.85 20.00   0.50 20.00   8.31 18.32
the:DT   0.00 20.00 20.00 20.00   0.00 20.50   0.00 20.50 20.50
lunar:NN 20.00 10.00   6.16 15.00 20.00 10.50 20.00   5.92 19.44
landscape:NN 20.00   9.81   9.22 14.96 20.00   8.94 20.00   8.45 19.16
its:PRP$ 20.00 12.00 12.00 15.00 20.00 12.50 20.00 12.50 20.50
black:JJ 20.00 12.00 12.00   8.57 20.00   9.47 20.00 12.50 20.34
horizon:NN 20.00   9.82   7.41 15.00 20.00 10.24 20.00   7.72 20.50
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.42 Alignment.score
 1.00  0.81 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.44 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added moon[moon-NNP]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "U.S." of "flag" dropped on aligned hyp word "American_flag"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1969
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: flag -> american_flag
-0.10  1.00 NullPunisher.article : the
-3.00  1.00 NullPunisher.entity : moon
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : waved
 1.00  1.00 Quant.contract : [a,the]
-2.00  1.00 RootEntailment.unalignedRoot : "waved" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.5334
Threshold: -1.8794


Inference ID: 600

Txt: Thus began the journey that led Hannam, a 29-year-old free-lance journalist, on an international quest to solve one of the greatest mysteries of Asia: What happened to Lin Piao, the Chinese Communist Party leader accused of a 1971 plot to overthrow Chairman Mao.

Hyp: Lin Piao was the Chinese Communist Party leader. (yes)

Lin_Piao
NNP
was
VBD
the
DT
Chinese_Communist_Party
NNP
leader
NN
Thus:RB 15.50 20.00 20.00 15.50 15.00
began:VBD 15.50 10.00 20.00 15.50 14.06
the:DT 20.50 20.00   0.00 20.50 20.00
journey:NN   8.83 15.00 20.00   9.15   6.35
that:WDT 20.50 20.00 10.00 20.50 20.00
led:VBD 15.50 10.00 20.00 15.50   2.50
Hannam:NNP 10.50 15.50 20.50 10.50 10.50
a:DT 20.50 20.00 10.00 20.50 20.00
29-year-old:JJ 12.50 12.00 20.00 12.50 12.00
free-lance:JJ 12.50 12.00 20.00 12.50 12.00
journalist:NN   8.07 15.00 20.00   9.36   5.16
an:DT 20.50 20.00 10.00 20.50 20.00
international:JJ 12.50 12.00 20.00 12.50 12.00
quest:NN   9.76 15.00 20.00   9.95   7.55
to:TO 20.50 20.00 10.00 20.50 20.00
solve:VB 15.50 10.00 20.00 15.50 15.00
one:CD 20.50 20.50 20.50 20.50 20.50
the:DT 20.50 20.00   0.00 20.50 20.00
greatest:JJS 12.50 12.00 20.00 12.50 10.54
mysteries:NNS   9.69 15.00 20.00   9.90   7.81
Asia:NNP   8.79 15.50 20.50   9.54   6.78
What:WP 12.50 15.00 20.00 12.50 12.00
happened:VBD 15.50 10.00 20.00 15.50 14.71
Lin_Piao:NNP   0.00 15.50 20.50   9.77   6.70
the:DT 20.50 20.00   0.00 20.50 20.00
Chinese_Communist_Party:NNP   9.77 15.50 20.50   0.00   8.44
leader:NN   6.70 15.00 20.00   8.44   0.00
accused:VBD 15.50 10.00 20.00 15.50 13.11
a:DT 20.50 20.00 10.00 20.50 20.00
1971:CD 20.50 20.50 20.50 20.50 19.57
plot:NN   9.31 15.00 20.00   9.57   7.13
to:TO 20.50 20.00 10.00 20.50 20.00
overthrow:VB 15.50 10.00 20.00 15.50 14.54
Chairman:NNP   7.48 15.00 20.00   8.98   1.43
Mao:NNP   9.38 15.50 20.50 10.33   9.04
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.88 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: accused-VBD
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Lin_Piao" <-prep_to-- "happened" vs. hyp "Lin_Piao" <-nsubj-- "leader", which aligned to text "leader" args have different parents, different relations: text "Chinese_Communist_Party" <-nsubj-- "accused" vs. hyp "Chinese_Communist_Party" <-nn-- "leader", which aligned to text "leader" args have different parents, different relations: text "Chinese_Communist_Party" <-xsubj-- "overthrow" vs. hyp "Chinese_Communist_Party" <-nn-- "leader", which aligned to text "leader"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -1.4393
Threshold: -1.8794


Inference ID: 2065

Txt: The slender tower is the second tallest building in Japan.

Hyp: The slender tower is the tallest building in Japan. (don't know)

The
DT
slender
NN
tower
NN
is
VBZ
the
DT
tallest
JJS
building
NN
Japan
NNP
The:DT   0.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
slender:NN 20.00   0.00   6.34 15.00 20.00   9.32   7.28 10.50
tower:NN 20.00   6.34   0.00 15.00 20.00   5.91   2.29   8.62
is:VBZ 20.00 15.00 15.00   0.00 20.00 12.00 15.00 15.50
the:DT   0.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
second:JJ 20.00 11.71 12.00 12.00 20.00   9.34 12.00 12.50
tallest:JJS 20.00   9.32   5.91 12.00 20.00   0.00   6.69 12.50
building:NN 20.00   7.28   2.29 15.00 20.00   6.69   0.00   7.06
Japan:NNP 20.50 10.50   8.62 15.50 20.50 12.50   7.06   0.00
NO_WORD   1.00 10.00 10.00   1.00   1.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.00 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  8.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
-5.00  1.00 Adjunct.superlative : text adjunct "second" of "building" dropped on aligned hyp word "building"
 0.00  1.00 NegPolarity.hypNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord&NegPolarity.hypNegWord :
Hand-tuned score (dot product of above): -1.1464
Threshold: -1.8794


Inference ID: 2059

Txt: At eurodisney, once upon a time is now in this magical kingdom where childhood fantasies and make-believe come to life.

Hyp: EuroDisney is located in this magical kingdom. (don't know)

EuroDisney
NNP
is
VBZ
located
VBN
this
DT
magical
JJ
kingdom
NN
eurodisney:NN   0.50 15.00 15.00 20.00 12.00 10.00
once:RB 15.50 20.00 20.00 20.00 12.00 15.00
a:DT 20.50 20.00 20.00 10.00 20.00 20.00
time:NN 10.50 15.00 15.00 20.00 10.83   8.24
is:VBZ 15.50   0.00 10.00 20.00 12.00 15.00
now:RB 15.50 20.00 20.00 20.00 12.00 15.00
this:DT 20.50 20.00 20.00   0.00 20.00 20.00
magical:JJ 12.50 12.00 11.72 20.00   0.00 12.00
kingdom:NN 10.50 15.00 13.39 20.00 12.00   0.00
where:WRB 20.50 20.00 20.00 10.00 20.00 20.00
childhood:NN 10.50 15.00 14.80 20.00   8.59   9.23
fantasies:NNS 10.50 15.00 15.00 20.00   8.24   9.12
make-believe:NN 10.50 15.00 14.81 20.00 12.00   9.60
come_to_life:VBP 15.50 10.00 10.00 20.00 12.00 15.00
NO_WORD 10.00   1.00 10.00 10.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.19 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 2.00  1.00 Location.match : "kingdom" PP coargument of "EuroDisney"
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : located
-2.00  1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
 4.00  1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 5.1891
Threshold: -1.8794


Inference ID: 1456

Txt: On his oak mantelpiece are a drinking bowl from Lake Turkana and an East African gourd, both of which he bought in Nairobi where he went in the fall of 1993 to visit Kitum Cave, the suspected host site for the Marburg virus, which is closely related to Ebola.

Hyp: The Ebola virus has been found in Zaire. (don't know)

The
DT
Ebola_virus
NN
has
VBZ
been
VBN
found
VBN
Zaire
NNP
his:PRP$ 20.00 12.50 15.00 15.00 15.00 12.50
oak:NN 20.00 10.50 15.00 15.00 14.60 10.44
mantelpiece:NN 20.00 10.50 15.00 15.00 15.00 10.50
are:VBP 20.00 15.50   8.78   0.00 10.00 15.50
a:DT 10.00 20.50 20.00 20.00 20.00 20.50
drinking:NN 20.00   5.92 15.00 15.00 10.99 10.44
bowl:NN 20.00 10.28 15.00 15.00 15.00 10.46
Lake_Turkana:NNP 20.50 10.00 15.50 15.50 15.50   9.79
an:DT 10.00 20.50 20.00 20.00 20.00 20.50
East_African:JJ 20.00 12.50 12.00 12.00 12.00 12.50
gourd:NN 20.00   8.75 15.00 15.00 15.00 10.50
both:DT 10.00 20.50 20.00 20.00 20.00 20.50
of:IN 20.00 20.50 20.00 20.00 20.00 20.50
which:WDT 10.00 20.50 20.00 20.00 20.00 20.50
his:PRP$ 20.00 12.50 15.00 15.00 15.00 12.50
bought:VBD 20.00 14.70 10.00 10.00   7.70 15.50
Nairobi:NNP 20.50 10.00 15.50 15.50 15.50 10.00
where:WRB 10.00 20.50 20.00 20.00 20.00 20.50
his:PRP$ 20.00 12.50 15.00 15.00 15.00 12.50
went:VBD 20.00 15.50 10.00 10.00   6.62 15.50
the:DT   0.00 20.50 20.00 20.00 20.00 20.50
fall:NN 20.50 10.50 15.50 15.50 14.75   8.63
1993:CD 20.50 20.50 20.50 20.50 17.22 20.50
to:TO 10.00 20.50 20.00 20.00 20.00 20.50
visit:VB 20.00 15.50 10.00 10.00   8.30 15.50
Kitum_Cave:NNP 20.50 10.50 15.50 15.50 15.50 10.48
the:DT   0.00 20.50 20.00 20.00 20.00 20.50
suspected:VBN 20.00 11.94 10.00 10.00   5.46 15.50
host:NN 20.00 10.50 15.00 15.00 15.00 10.38
site:NN 20.00   8.93 15.00 15.00 13.81 10.28
the:DT   0.00 20.50 20.00 20.00 20.00 20.50
Marburg_virus:NN 20.50   0.00 15.50 15.50 10.91 10.00
which:WDT 10.00 20.50 20.00 20.00 20.00 20.50
is:VBZ 20.00 15.50   8.64   0.00 10.00 15.50
closely:RB 20.00 14.59 20.00 20.00 18.77 15.50
related:VBN 20.00 14.59 10.00 10.00   8.53 15.50
Ebola:NNP 20.50   5.00 15.50 15.50 15.50 10.00
NO_WORD   1.00 10.00   1.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.59 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Zaire[Zaire-NNP]
-0.05  1.00 NullPunisher.aux : has
-0.10  1.00 NullPunisher.article : The
-0.05  1.00 NullPunisher.aux : been
-1.00  1.00 NullPunisher.other : found
-3.00  1.00 NullPunisher.entity : Zaire
-2.00  1.00 RootEntailment.unalignedRoot : "found" not aligned to anything
Hand-tuned score (dot product of above): -6.1690
Threshold: -1.8794


Inference ID: 2046

Txt: Hoover's director reports directly to Stanford's president.

Hyp: Hoover is the president of Stanford University. (don't know)

Hoover
NNP
is
VBZ
the
DT
president
NN
Stanford_University
NNP
Hoover:NNP   0.50 15.50 20.50   8.81   9.76
director:NN   8.35 15.00 20.00   1.99   9.04
reports:VBZ 15.00 10.00 20.00 15.00 15.50
directly:RB 15.00 20.00 20.00 14.63 15.50
Stanford:NNP   6.01 15.50 20.50   8.52   5.00
president:NN   8.31 15.00 20.00   0.00   8.93
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.18 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: reports-VBZ
-0.10  1.00 NullPunisher.article : the
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 Structure.relMismatch : text "Stanford" is poss of "president" while hyp "Stanford_University" is prep_of of "president" which aligned to text "president"
Hand-tuned score (dot product of above): 1.4655
Threshold: -1.8794


Inference ID: 628

Txt: The human species may be more than 100,000 years old but the bulk of its numerical growth has come in just the last few decades: 2.6 billion of today's 5.6 billion people have been born since 1960.

Hyp: The world population is 5.6 billion. (yes)

The
DT
world
NN
population
NN
is
VBZ
5.6
CD
billion
CD
The:DT   0.00 20.00 20.00 20.00 20.50 20.50
human:JJ 20.00   9.54   9.33 12.00 19.56 20.25
species:NNS 20.00   8.27   7.35 15.00 20.50 20.50
may:MD 10.00 20.00 20.00 20.00 20.50 20.50
be:VB 20.00 15.00 15.00   0.00 20.50 20.50
more_than:IN 20.00 20.00 20.00 20.00 20.50 20.50
100,000:CD 20.50 20.50 18.92 20.50   5.00   5.00
years:NNS 20.00   7.50   8.42 15.00 20.50 19.73
old:JJ 20.00 11.33 12.00 12.00 20.50 19.85
the:DT   0.00 20.00 20.00 20.00 20.50 20.50
bulk:NN 20.00   8.28   9.00 15.00 19.87 20.08
its:PRP$ 20.00 12.00 12.00 13.00 20.50 20.50
numerical:JJ 20.00 11.10 11.24 12.00 20.50 20.50
growth:NN 20.00   7.74   7.22 15.00 17.90 20.50
has:VBZ 20.00 15.00 15.00   8.64 20.50 20.50
come:VBN 20.00 13.32 14.66 10.00 20.50 19.32
just:RB 20.00 14.31 14.56 20.00 19.29 19.78
the:DT   0.00 20.00 20.00 20.00 20.50 20.50
last:JJ 20.00 12.00 12.00 12.00 20.50 20.50
few:JJ 20.00 12.00 12.00 12.00 20.50 20.50
decades:NNS 20.00   8.17   8.14 15.00 20.22 20.50
2.6:CD 20.50 19.37 18.34 20.50   2.01   5.00
billion:CD 20.50 19.69 20.50 20.50   5.00   0.00
today:NN 20.00   8.01   8.81 15.00 18.86 20.50
5.6:CD 20.50 19.99 18.12 20.50   0.00   0.00
billion:CD 20.50 19.69 20.50 20.50   0.00   0.00
people:NNS 20.00   5.08   4.24 15.00 19.85 20.50
have:VBP 20.00 15.00 15.00 10.00 20.50 20.50
been:VBN 20.00 15.00 15.00   0.00 20.50 20.50
born:VBN 20.00 12.51 12.36 10.00 20.50 19.86
1960:CD 20.50 17.80 18.72 20.50   9.19 10.50
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.65 Alignment.score
 1.00  0.84 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added world[world-NN]
-1.00  1.00 Hypernym.posNarrow : narrowing in positive context: people -> population
-0.10  1.00 NullPunisher.article : The
-1.00  1.00 NullPunisher.other : world
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "people" <-prep_of-- "2.6" vs. hyp "population" <-nsubj-- "5.6", which aligned to text "5.6"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -6.7112
Threshold: -1.8794


Inference ID: 2080

Txt: VCU School of the Arts In Qatar is located in Doha, the capital city of Qatar.

Hyp: Qatar is located in Doha. (don't know)

Qatar
NNP
is
VBZ
located
VBN
Doha
NNP
VCU_School_of_the_Arts_In_Qatar:NNP 10.50 15.50 15.50 10.50
is:VBZ 15.00   0.00 10.00 15.50
located:VBN 15.00 10.00   0.00 15.50
Doha:NNP   8.75 15.50 15.50   0.00
the:DT 20.00 20.00 20.00 20.50
capital:NN 10.00 15.00 13.69 10.50
city:NN 10.00 15.00 11.25 10.50
Qatar:NNP   0.50 15.50 15.50   8.25
NO_WORD 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.68 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "city" of "Doha" dropped on aligned hyp word "Doha"
-2.00  1.00 Location.mismatch : no clear info of matching: locate(X, prep_in)
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Qatar" <-prep_of-- "city" vs. hyp "Qatar" <-nsubjpass-- "located", which aligned to text "located"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.9099
Threshold: -1.8794


Inference ID: 617

Txt: In 1954, in a gesture of friendship to mark the 300th anniversary of Ukrainian union with Russia, Soviet Premier Nikita Khrushchev gave Crimea to Ukraine.

Hyp: Crimea became part of Ukraine in 1954. (yes)

Crimea
NNP
became
VBD
part
NN
Ukraine
NNP
1954
CD
1954:CD 20.50 20.50 19.23 20.50   0.00
a:DT 20.50 20.00 20.00 20.50 20.50
gesture:NN 10.50 15.00   7.73 10.29 20.50
friendship:NN 10.50 15.00   5.99   9.44 19.37
to:TO 20.50 20.00 20.00 20.50 20.50
mark:VB 15.50 10.00 15.00 15.50 20.46
the:DT 20.50 20.00 20.00 20.50 20.50
300th:JJ 12.50 12.00 12.00 12.50 19.84
anniversary:NN 10.50 15.00   7.33 10.24 18.04
Ukrainian:NNP 10.00 15.50 10.50   3.00 20.50
union:NN 10.50 15.00   7.12   9.92 20.05
Russia:NNP 10.00 15.50   8.72   6.43 20.50
Soviet:NNP 10.50 15.00   7.57 10.08 20.50
Premier:NNP 10.50 15.00   7.96   9.87 20.50
Nikita_Khrushchev:NNP 10.50 15.50 10.50 10.50 20.50
gave:VBD 15.50 10.00 13.64 15.50 17.25
Crimea:NNP   0.00 15.50 10.50 10.00 20.50
Ukraine:NNP 10.00 15.50   9.38   0.00 20.50
NO_WORD 10.00   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.20 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1954
-1.00  1.00 NullPunisher.other : part
-1.00  1.00 NullPunisher.other : became
-2.00  1.00 RootEntailment.unalignedRoot : "part" not aligned to anything
Hand-tuned score (dot product of above): -1.3564
Threshold: -1.8794


Inference ID: 2060

Txt: On this particular trip, Airbus, in cooperation with its partners, organized a trip for 80 children between the ages of 9 and 16 years from nearby National Children's Homes to visit EuroDisney for a day.

Hyp: EuroDisney is located nearby National Children's Homes. (don't know)

EuroDisney
NNP
is
VBZ
located
VBN
nearby
RB
National_Children
NNP
Homes
NNPS
this:DT 20.50 20.00 20.00 20.00 20.50 20.00
particular:JJ 12.50 12.00 12.00 12.00 12.50 12.00
trip:NN 10.50 15.00 12.33 12.29   8.68   8.20
Airbus:NNP 10.50 15.50 15.50 15.50   9.22   9.33
cooperation:NN 10.50 15.00 13.73 15.00   8.02   7.54
its:PRP$ 12.50 13.00 15.00 20.00 12.50 12.00
partners:NNS 10.50 15.00 13.94 15.00   7.10   7.36
organized:VBD 15.50 10.00 10.00 19.16 15.50 15.00
a:DT 20.50 20.00 20.00 20.00 20.50 20.00
trip:NN 10.50 15.00 12.33 12.29   8.68   8.20
80:CD 20.50 20.50 19.70 19.83 20.50 20.50
children:NNS 10.50 15.00 15.00 13.01   5.50   7.71
the:DT 20.50 20.00 20.00 20.00 20.50 20.00
ages:NNS 10.50 15.00 14.85 14.15   8.80   8.32
9:CD 20.50 20.50 20.50 19.93 20.50 20.50
16:CD 20.50 20.50 20.50 20.50 20.50 20.50
years:NNS 10.50 15.00 13.67 13.86   7.99   7.51
nearby:JJ 12.50 12.00   5.47   0.00 12.50 12.00
National_Children:NNP 10.00 15.50 15.50 15.50   0.00   7.81
Homes:NNPS 10.50 15.00 15.00 15.00   7.81   0.00
to:TO 20.50 20.00 20.00 20.00 20.50 20.00
visit:VB 15.50 10.00   7.90 18.07 15.50 15.00
EuroDisney:NNP   0.00 15.50 15.50 15.50 10.00 10.50
a:DT 20.50 20.00 20.00 20.00 20.50 20.00
day:NN 10.50 15.00 15.00 13.01   7.89   7.41
NO_WORD 10.00   1.00 10.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.68 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "day" of "EuroDisney" dropped on aligned hyp word "EuroDisney"
-1.00  1.00 NullPunisher.other : located
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.7113
Threshold: -1.8794


Inference ID: 1493

Txt: "We've never had a core (business) thing," founder Richard Branson told Reuters at the launch this month of Virgin Vodka, which, with Virgin cola and Virgin computers, are his latest bright ideas.

Hyp: Richard Branson's cola is called Virgin Cola. (yes)

Richard_Branson
NNP
cola
NN
is
VBZ
called
VBN
Virgin_Cola
NNP
We:PRP 12.50 12.00 15.00 15.00 12.50
've:VBP 15.50 15.00 10.00 10.00 15.50
never:RB 15.50 15.00 20.00 20.00 15.50
had:VBN 15.50 15.00 10.00 10.00 15.50
a:DT 20.50 20.00 20.00 20.00 20.50
core:NN 10.50   9.54 15.00 14.08   9.83
business:NN 10.50   7.41 15.00 15.00   8.98
thing:NN 10.50   9.40 15.00 15.00   9.92
founder:NNP 10.50   9.26 15.00 12.19 10.09
Richard_Branson:NNP   0.00 10.50 15.50 15.50 10.00
told:VBD 15.50 15.00 10.00   5.92 15.50
Reuters:NNP 10.50 10.50 15.50 15.50 10.50
the:DT 20.50 20.00 20.00 20.00 20.50
launch:NN 10.50   8.82 15.00 11.12   9.68
this:DT 20.50 20.00 20.00 20.00 20.50
month:NN 10.50   8.62 15.00 13.84   9.15
Virgin_Vodka:NNP 10.00 10.30 15.50 15.50   0.00
which:WDT 20.50 20.00 20.00 20.00 20.50
Virgin:NNP 10.00 10.30 15.50 15.50   0.00
cola:NNP 10.50   0.00 15.00 15.00 10.30
Virgin:NNP 10.00 10.30 15.50 15.50   0.00
computers:NNS 10.50   8.96 15.00 13.00   9.05
are:VBP 15.50 15.00   0.00 10.00 15.50
his:PRP$ 12.50 12.00 13.00 15.00 12.50
latest:JJS 12.50 12.00 12.00   9.33 12.50
bright:JJ 12.50 12.00 12.00 11.22 12.50
ideas:NNS 10.50   8.20 15.00 13.88   8.74
NO_WORD 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.30 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Virgin" of "cola" dropped on aligned hyp word "cola"
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : called
-2.00  1.00 Person.mismatch : person mimatch between Virgin_Cola and Virgin_Vodka
-2.00  1.00 RootEntailment.unalignedRoot : "called" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.8247
Threshold: -1.8794


Inference ID: 2061

Txt: Although Eurodisney is a great family day out, visit also the chateaux in the area and the various parks and pretty small villages.

Hyp: EuroDisney is located in the various parks. (don't know)

EuroDisney
NNP
is
VBZ
located
VBN
the
DT
various
JJ
parks
NNS
Although:IN 20.50 20.00 20.00 20.00 20.00 20.00
Eurodisney:NNP   0.00 15.50 15.50 20.50 12.50 10.50
is:VBZ 15.50   0.00 10.00 20.00 12.00 15.00
a:DT 20.50 20.00 20.00 10.00 20.00 20.00
great:JJ 12.50 12.00 12.00 20.00   9.54 10.37
family:NN 10.50 15.00 15.00 20.00 12.00   8.66
day:NN 10.50 15.00 15.00 20.00 12.00   8.58
out:RB 15.50 20.00 20.00 20.00 12.00 15.00
visit:VB 15.50 10.00   7.90 20.00 12.00 15.00
also:RB 15.50 20.00 20.00 20.00 12.00 15.00
the:DT 20.50 20.00 20.00   0.00 20.00 20.00
chateaux:NN 10.50 15.00 13.95 20.00 11.12   9.22
the:DT 20.50 20.00 20.00   0.00 20.00 20.00
area:NN 10.50 15.00   7.90 20.00 11.57   6.70
the:DT 20.50 20.00 20.00   0.00 20.00 20.00
various:JJ 12.50 12.00 11.95 20.00   0.00   9.80
parks:NNS 10.50 15.00 10.85 20.00   9.80   0.00
pretty:RB 15.50 20.00 20.00 20.00 12.00 14.56
small:JJ 12.50 12.00   9.32 20.00   6.77 12.00
villages:NNS 10.50 15.00 11.52 20.00 10.04   8.21
NO_WORD 10.00   1.00 10.00   1.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.34 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-2.00  1.00 Location.mismatch : no clear info of matching: locate(X, prep_in)
-1.00  1.00 NullPunisher.other : located
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
Hand-tuned score (dot product of above): -2.6454
Threshold: -1.8794


Inference ID: 595

Txt: Brown brushed off a threat made Monday by his counterpart, Foreign Trade Minister Wu Yi, that if China's effort to join the General Agreement on Tariffs and Trade (GATT) by the year's end is unsuccessful, Beijing will no longer be bound by previous trade and economic commitments.

Hyp: Wu Yi is the Foreign Trade Minister of China. (yes)

Wu_Yi
NNP
is
VBZ
the
DT
Foreign
NNP
Trade
NNP
Minister
NNP
China
NNP
Brown:NNP   9.58 15.00 20.00   8.78   8.22   8.61   8.98
brushed_off:VBD 15.50 10.00 20.00 15.00 15.00 15.00 15.50
a:DT 20.50 20.00 10.00 20.00 20.00 20.00 20.50
threat:NN 10.33 15.00 20.00   9.04   8.56   8.05   8.92
made:VBN 15.50 10.00 20.00 15.00 10.00 15.00 15.50
Monday:NNP   9.57 15.50 20.50   9.21   8.64   9.04   8.90
his:PRP$ 12.50 13.00 20.00 12.00 12.00 12.00 12.50
counterpart:NN 10.38 15.00 20.00   9.20   8.76   9.06   9.46
Foreign:NNP 10.34 15.00 20.00   0.00   8.59   8.92   9.31
Trade:NNP 10.14 15.00 20.00   8.59   0.00   8.40   8.77
Minister:NNP 10.28 15.00 20.00   8.92   8.40   0.00   8.75
Wu_Yi:NNP   0.00 15.50 20.50 10.34 10.14 10.28 10.24
that:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.50
if:IN 20.50 20.00 17.77 20.00 20.00 20.00 20.50
China:NNP 10.24 15.50 20.50   9.31   8.77   8.75   0.00
effort:NN   9.75 15.00 20.00   7.78   5.73   7.54   7.86
to:TO 20.50 20.00 10.00 20.00 20.00 20.00 20.50
join:VB 15.50 10.00 20.00 15.00 15.00 15.00 15.50
the:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50
General_Agreement_on_Tariffs_and_Trade:NNP 10.46 15.50 20.50   9.98   9.64   9.88   9.80
GATT:NNP 10.46 15.50 20.50   9.98   9.64   9.88   9.80
the:DT 20.50 20.00   0.00 20.00 20.00 20.00 20.50
year:NN   9.46 15.00 20.00   8.07   7.35   7.84   8.18
end:NN 10.03 15.00 20.00   8.33   7.67   7.65   6.62
is:VBZ 15.50   0.00 20.00 15.00 15.00 15.00 15.50
unsuccessful:JJ 12.50 12.00 20.00 12.00 12.00 12.00 12.50
Beijing:NNP 10.36 15.50 20.50   9.65   9.20   9.19   6.28
will:MD 20.50 20.00 10.00 20.00 20.00 20.00 20.50
no:RB 15.50 20.00 20.00 15.00 15.00 15.00 15.50
longer:RB 15.50 20.00 20.00 15.00 15.00 15.00 15.50
be:VB 15.50   0.00 20.00 15.00 15.00 15.00 15.50
bound:VBN 15.50 10.00 20.00 15.00 15.00 15.00 15.50
previous:JJ 12.50 12.00 20.00 12.00 12.00 12.00 12.50
trade:NN 10.14 15.00 20.00   8.59   0.00   8.40   8.77
economic:JJ 12.50 12.00 20.00 12.00 12.00 12.00 12.50
commitments:NNS   9.62 15.00 20.00   9.01   8.52   8.86   9.25
NO_WORD 10.00   1.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.72 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.57 Alignment.txtSpan
-1.00  1.00 Apposition.mismatch : no apposition in text between Minister and Wu_Yi
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Wu_Yi" <-prep_by-- "made" vs. hyp "Wu_Yi" <-nsubj-- "Minister", which aligned to text "Minister" args have different parents, different relations: text "Foreign" <-appos-- "counterpart" vs. hyp "Foreign" <-nn-- "Minister", which aligned to text "Minister" args have different parents, different relations: text "Trade" <-appos-- "counterpart" vs. hyp "Trade" <-nn-- "Minister", which aligned to text "Minister" args have different parents, different relations: text "China" <-poss-- "effort" vs. hyp "China" <-prep_of-- "Minister", which aligned to text "Minister"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -3.2309
Threshold: -1.8794


Inference ID: 1454

Txt: In fact, Woolsey had had no first-hand experience with the world of spies until President Bill Clinton appointed him Director of Central Intelligence.

Hyp: James Woolsey is the director of the CIA. (yes)

James_Woolsey
NNP
is
VBZ
the
DT
director
NN
the
DT
CIA
NNP
fact:NN   8.29 15.00 20.00   7.20 20.00   8.94
Woolsey:NNP   5.00 15.50 20.50 10.50 20.50 10.50
had:VBD 15.50 10.00 20.00 15.00 20.00 15.50
had:VBN 15.50 10.00 20.00 15.00 20.00 15.50
no:DT 20.50 20.00 10.00 20.00 10.00 20.50
first-hand:JJ 12.50 12.00 20.00 12.00 20.00 12.50
experience:NN   9.35 15.00 20.00   8.44 20.00   9.77
the:DT 20.50 20.00   0.00 20.00   0.00 20.50
world:NN   8.70 15.00 20.00   7.67 20.00   8.35
spies:NNS   8.75 15.00 20.00   7.82 20.00   9.96
until:IN 20.50 20.00 20.00 20.00 20.00 20.50
President:NNP   7.23 15.00 20.00   1.99 20.00   9.06
Bill_Clinton:NNP   8.33 15.50 20.50   8.59 20.50 10.08
appointed:VBD 15.50 10.00 20.00 12.30 20.00 15.50
him:PRP 12.50 15.00 20.00 12.00 20.00 12.50
Director_of_Central_Intelligence:NP 20.50 20.00 10.00 20.00 10.00 20.50
NO_WORD 10.00   1.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.59 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added CIA[CIA-NNP]
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : director
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 NullPunisher.entity : CIA
-0.10  1.00 NullPunisher.article : the
-2.00  1.00 RootEntailment.unalignedRoot : "director" not aligned to anything
Hand-tuned score (dot product of above): -6.2134
Threshold: -1.8794


Inference ID: 622

Txt: Witnesses of genocide attacks on minority Tutsis in Rwanda are being singled out for execution by extremist Hutus in refugee camps in southwest Rwanda, a U.N. spokesman said in Kigali, the capital.

Hyp: The Hutu and Tutsi groups fought in Rwanda. (yes)

The
DT
Hutu
NNP
Tutsi
NNP
groups
NNS
fought
VBD
Rwanda
NNP
Witnesses:NNS 20.00 10.00 10.00   7.04 15.00   9.90
genocide:NN 20.00 10.00 10.00   7.89 12.21   9.65
attacks:NNS 20.00 10.00 10.00   5.58 13.56   9.94
minority:NN 20.00   9.30   9.12   4.71 13.84 10.22
Tutsis:NNS 20.00   8.36   1.00 10.00 15.00   9.59
Rwanda:NNP 20.50 10.50   9.81   8.85 15.50   0.00
are:VBP 20.00 15.00 15.00 15.00 10.00 15.50
being:VBG 20.00 15.00 15.00 15.00 10.00 15.50
singled_out:VBN 20.00 15.00 15.00 15.00 10.00 15.50
execution:NN 20.00 10.00 10.00   6.94 13.37 10.28
extremist:NN 20.00   9.11   9.29   5.74 12.31   9.96
Hutus:NNS 20.00   1.00   7.96 10.00 15.00   9.73
refugee_camps:NNS 20.00 10.00 10.00   7.30 15.00   9.91
southwest:JJ 20.00 12.00 12.00 12.00 12.00 12.50
Rwanda:NNP 20.50 10.50   9.81   8.85 15.50   0.00
a:DT 10.00 20.00 20.00 20.00 20.00 20.50
U.N.:NNP 20.50 10.50 10.50   7.88 15.50 10.36
spokesman:NN 20.00 10.00 10.00   5.91 15.00   9.82
said:VBD 20.00 15.00 15.00 15.00   9.97 15.50
Kigali:NNP 20.50 10.50 10.50 10.50 15.50   8.92
the:DT   0.00 20.00 20.00 20.00 20.00 20.50
capital:NN 20.00 10.00 10.00   4.86 14.77   9.72
NO_WORD   1.00 10.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.01 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "minority" of "Tutsis" dropped on aligned hyp word "Tutsi"
-1.00  1.00 NullPunisher.other : groups
-0.10  1.00 NullPunisher.article : The
-1.00  1.00 NullPunisher.other : fought
-2.00  1.00 RootEntailment.unalignedRoot : "fought" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.2776
Threshold: -1.8794


Inference ID: 2089

Txt: Prince Charles was previously married to Princess Diana, who died in a car crash in Paris in August 1997.

Hyp: Prince Charles and Princess Diana got married in August 1997. (don't know)

Prince_Charles
NNP
Princess_Diana
NNP
got
VBD
married
JJ
August
NNP
1997
CD
Prince_Charles:NNP   0.00   3.45 15.00 12.00   9.15 20.50
was:VBD 15.00 15.00 10.00 12.00 15.50 20.50
previously:RB 15.00 15.00 20.00 12.00 15.50 20.50
married:JJ 12.00 12.00   9.96   0.00 12.50 20.50
Princess_Diana:NNP   3.45   0.00 15.00 12.00   9.84 20.50
who:WP 12.00 12.00 15.00 15.00 12.50 20.50
died:VBD 15.00 15.00   9.46   6.64 15.50 20.50
a:DT 20.00 20.00 20.00 20.00 20.50 20.50
car:NN   7.72   8.70 13.60 10.50   8.31 20.32
crash:NN   8.88   9.48 13.94 12.00   9.00 20.50
Paris:NNP   9.23   9.84 15.50 12.50   9.20 20.50
August:NNP   9.15   9.84 15.50 12.50   0.00 20.00
1997:CD 20.50 20.50 20.50 20.50 20.00   0.00
NO_WORD 10.00 10.00   1.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.75 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "previously" of "married" dropped on aligned hyp word "married"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 08/01/1997
-1.00  1.00 NullPunisher.other : got
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "August" <-prep_in-- "died" vs. hyp "August" <-prep_in-- "married", which aligned to text "married"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.5816
Threshold: -1.8794


Inference ID: 1474

Txt: To avoid further price-gouging, Norway's Ministry of Government Administration announced that it was freezing prices on lodging, food and beverages in the Lillehammer area during the Winter Olympics.

Hyp: Norway hosted the Winter Olympic Games. (yes)

Norway
NNP
hosted
VBD
the
DT
Winter_Olympic_Games
NNPS
To:TO 20.50 20.00 10.00 20.00
avoid:VB 15.50 10.00 20.00 15.00
further:JJ 12.50 12.00 20.00 12.00
price-gouging:NN 10.50 15.00 20.00 10.00
Norway:NNP   0.00 15.50 20.50 10.50
Ministry_of_Government_Administration:NNP   9.91 15.50 20.50 10.50
announced:VBD 15.50   7.06 20.00 15.00
that:IN 20.50 20.00 20.00 20.00
it:PRP 12.50 15.00 20.00 12.00
was:VBD 15.50 10.00 20.00 15.00
freezing:NN 10.21 15.00 20.00 10.00
prices:NNS   9.24 15.00 20.00 10.00
lodging:NN   8.87 11.79 20.00 10.00
food:NN   8.54 15.00 20.00 10.00
beverages:NNS   9.15 15.00 20.00 10.00
the:DT 20.50 20.00   0.00 20.00
Lillehammer:NNP 10.00 15.50 20.50 10.50
area:NN   7.01 13.77 20.00 10.00
the:DT 20.50 20.00   0.00 20.00
Winter_Olympics:NNPS 10.50 15.00 20.00   5.00
NO_WORD 10.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.98 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 NullPunisher.other : hosted
-2.00  1.00 RootEntailment.unalignedRoot : "hosted" not aligned to anything
Hand-tuned score (dot product of above): -0.9895
Threshold: -1.8794


Inference ID: 2057

Txt: The Hotel Moulin de Paris in 2.5km away from Disneyland Paris, and is located in the charming city of Magny-Le-Hongre.

Hyp: EuroDisney is located in the charming city of Magny-Le-Hongre. (don't know)

EuroDisney
NNP
is
VBZ
located
VBN
the
DT
charming
JJ
city
NN
Magny-Le-Hongre
NNP
The:DT 20.50 20.00 20.00   0.00 20.00 20.00 20.50
Hotel:NNP 10.50 15.00 15.00 20.00 12.00   6.37   9.64
Moulin:NNP 10.50 15.00 15.00 20.00 12.00 10.00 10.50
de:IN 20.50 20.00 18.74 20.00 19.97 19.00 20.50
Paris:NNP 10.50 15.50 15.50 20.50 12.50   3.75 10.04
2.5:CD 20.50 20.50 20.50 20.50 20.21 20.44 20.50
km:VBP 15.50 10.00   7.33 20.00 11.49 15.00 15.50
Disneyland_Paris:NNP 10.50 15.50 15.50 20.50 12.50   6.15 10.37
is:VBZ 15.50   0.00 10.00 20.00 12.00 15.00 15.50
located:VBN 15.50 10.00   0.00 20.00 11.97 11.25 15.50
the:DT 20.50 20.00 20.00   0.00 20.00 20.00 20.50
charming:JJ 12.50 12.00 11.97 20.00   0.00 11.57 12.50
city:NN 10.50 15.00 11.25 20.00 11.57   0.00   9.20
Magny-Le-Hongre:NNP 10.00 15.50 15.50 20.50 12.50   9.20   0.00
NO_WORD 10.00   1.00 10.00   1.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.63 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.86 Alignment.txtSpan
-2.00  1.00 Location.mismatch : no clear info of matching: locate(X, prep_in)
-3.00  1.00 NullPunisher.entity : EuroDisney
Hand-tuned score (dot product of above): -1.8534
Threshold: -1.8794


Inference ID: 1453

Txt: The county Board of Supervisors accepted more than $700,000 in state grants Tuesday to extend a program that provides the controversial drug azidothymidine -- AZT -- to low-income residents with AIDS or AIDS-related illnesses.

Hyp: AZT is an AIDS treatment. (yes)

AZT
NNP
is
VBZ
an
DT
AIDS
NNP
treatment
NN
The:DT 20.50 20.00 10.00 20.00 20.00
county:NN   9.40 15.00 20.00   9.00   8.28
Board_of_Supervisors:NNP   8.87 15.50 20.50   9.14   8.29
accepted:VBD 15.50 10.00 20.00 15.00 15.00
more_than:$ 20.50 20.00 10.00 20.00 20.00
$:$ 20.50 20.50 10.50 20.50 20.50
700,000:CD 20.50 20.50 20.50 20.50 20.50
state:NN   9.29 15.00 20.00   8.55   7.68
grants:NNS   9.80 15.00 20.00   9.13   8.35
Tuesday:NNP   9.63 15.50 20.50   9.44   8.70
to:TO 20.50 20.00   8.20 20.00 20.00
extend:VB 15.50 10.00 20.00 15.00 13.71
a:DT 20.50 20.00   8.73 20.00 20.00
program:NN   9.34 15.00 20.00   8.60   7.75
that:WDT 20.50 20.00 10.00 20.00 20.00
provides:VBZ 15.50 10.00 20.00 15.00 13.60
the:DT 20.50 20.00 10.00 20.00 20.00
controversial:JJ 12.50 12.00 20.00 12.00   9.66
drug:NN   3.62 15.00 20.00   8.34   2.82
azidothymidine:NN 10.50 15.00 20.00 10.00 10.00
AZT:NNP   0.50 15.00 20.00   9.43   8.90
low-income:JJ 12.50 12.00 20.00 12.00 12.00
residents:NNS   8.87 15.00 20.00   9.07   8.38
AIDS:NNP   9.93 15.00 20.00   0.00   8.67
AIDS-related:JJ 12.50 12.00 20.00   7.00 10.18
illnesses:NNS   9.28 15.00 20.00   2.09   3.42
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.09 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : treatment
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "treatment" not aligned to anything
Hand-tuned score (dot product of above): -1.3492
Threshold: -1.8794


Inference ID: 1486

Txt: Now it's Romania-Argentina at the Rose Bowl today -- and with a victory, it's on to the quarterfinals at Stanford, and a victory there and Romania will be back in Pasadena for the semifinals and World Cup final.

Hyp: The World Cup final was held in Pasadena. (yes)

The
DT
World_Cup
NNP
final
NNP
was
VBD
held
VBN
Pasadena
NNP
Now:RB 20.00 15.00 15.00 20.00 20.00 15.50
it:PRP 20.00 12.00 12.00 15.00 15.00 12.50
's:VBZ 20.00 15.00 13.53   0.00   8.16 15.50
Romania-Argentina:NNP 20.50 10.50 10.43 15.50 15.50   7.92
the:DT   0.00 20.00 20.00 20.00 20.00 20.50
Rose_Bowl:NNP 20.50 10.50 10.19 15.50 15.50   9.85
today:NN 20.50 10.50   9.01 15.50 13.20   9.70
a:DT 10.00 20.00 20.00 20.00 20.00 20.50
victory:NN 20.00 10.00   4.76 15.00 12.34   9.81
it:PRP 20.00 12.00 12.00 15.00 15.00 12.50
's:VBZ 20.00 15.00 13.53   0.00   8.16 15.50
on:IN 20.00 20.00 20.00 20.00 20.00 20.50
the:DT   0.00 20.00 20.00 20.00 20.00 20.50
quarterfinals:NNS 20.00 10.00   5.00 15.00 15.00 10.50
Stanford:NNP 20.50 10.50 10.29 15.50 15.50   9.38
a:DT 10.00 20.00 20.00 20.00 20.00 20.50
victory:NN 20.00 10.00   4.76 15.00 12.34   9.81
there:EX 10.00 20.00 20.00 20.00 20.00 20.50
Romania:NNP 20.50 10.50 10.43 15.50 15.50   7.42
will:MD 10.00 20.00 20.00 20.00 20.00 20.50
be:VB 20.00 15.00 15.00   0.00 10.00 15.50
back:RB 20.00 15.00 14.58 20.00 18.00 15.50
Pasadena:NNP 20.50 10.50 10.30 15.50 15.50   0.00
the:DT   0.00 20.00 20.00 20.00 20.00 20.50
semifinals:NNS 20.00 10.00   2.03 15.00 14.58 10.46
World_Cup:NNP 20.00   0.00 10.00 15.00 15.00 10.50
final:JJ 20.00 12.00   0.00 12.00 10.13 12.50
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.26 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : held
-2.00  1.00 RootEntailment.unalignedRoot : "held" not aligned to anything
Hand-tuned score (dot product of above): -0.7132
Threshold: -1.8794


Inference ID: 2044

Txt: Jacqueline B. Wender is Assistant to the President of Stanford University.

Hyp: Jacqueline B. Wender is the President of Stanford University. (don't know)

Jacqueline_B._Wender
NNP
is
VBZ
the
DT
President
NNP
Stanford_University
NNP
Jacqueline_B._Wender:NNP   0.00 15.50 20.50   8.60   9.87
is:VBZ 15.50   0.00 20.00 15.00 15.50
Assistant:NNP   8.62 15.00 20.00   5.81   8.94
the:DT 20.50 20.00   0.00 20.00 20.50
President:NNP   8.60 15.00 20.00   0.00   8.93
Stanford_University:NNP   9.87 15.50 20.50   8.93   0.00
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.84 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Jacqueline_B._Wender" <-nsubj-- "Assistant" vs. hyp "Jacqueline_B._Wender" <-nsubj-- "President", which aligned to text "President"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -3.0026
Threshold: -1.8794


Inference ID: 642

Txt: For hundreds of family members holed up in a hotel not far from the Stockholm ferry terminal, where the boat Estonia was to have docked Wednesday morning after an overnight trip from the Estonian capital of Tallinn, the suspended rescue effort was just the latest in a day of heartbreaking reports.

Hyp: The Estonia set sail from Tallinn. (yes)

The
DT
Estonia
NNP
set
VBD
sail
JJ
Tallinn
NNP
hundreds:NNS 20.00 10.10 13.21 11.40 10.50
family:NN 20.00   9.82 15.00 11.38 10.50
members:NNS 20.00   9.60 13.65 11.83 10.50
holed_up:VBN 20.00 15.50 10.00 12.00 15.50
a:DT 10.00 20.50 20.00 20.00 20.50
hotel:NN 20.00   9.70 15.00 10.18 10.50
not:RB 20.00 15.50 20.00 12.00 15.50
far:RB 20.00 15.50 18.15 10.06 15.50
the:DT   0.00 20.50 20.00 20.00 20.50
Stockholm:NNP 20.50   6.94 15.50 12.50 10.00
ferry:NN 20.00   9.94 14.04   8.61 10.50
terminal:NN 20.00   9.90 13.28   8.25 10.50
where:WRB 10.00 20.50 20.00 20.00 20.50
the:DT   0.00 20.50 20.00 20.00 20.50
boat:NN 20.00   9.77 15.00   6.88 10.50
Estonia:NNP 20.50   0.00 15.50 12.50 10.00
was:VBD 20.00 15.50 10.00 12.00 15.50
to:TO 10.00 20.50 20.00 20.00 20.50
have:VB 20.00 15.50   7.27 12.00 15.50
docked:VBN 20.00 15.50 10.00   8.73 15.50
Wednesday:NNP 20.50 10.18 15.50 12.50 10.50
morning:NN 20.50 10.19 15.19 11.64 10.50
an:DT 10.00 20.50 20.00 20.00 20.50
overnight:JJ 20.00 12.50 10.36   9.76 12.50
trip:NN 20.00 10.11 14.94   8.20 10.50
the:DT   0.00 20.50 20.00 20.00 20.50
Estonian:JJ 20.00   5.17 12.00 10.00 12.50
capital:NN 20.00   9.72 15.00 10.97 10.50
Tallinn:NNP 20.50 10.00 15.50 12.50   0.00
the:DT   0.00 20.50 20.00 20.00 20.50
suspended:JJ 20.00 12.50 10.16 10.00 12.50
rescue:NN 20.00 10.10 13.85 10.05 10.50
effort:NN 20.00   9.53 13.11 11.63 10.50
was:VBD 20.00 15.50 10.00 12.00 15.50
just:RB 20.00 15.50 19.91   9.60 15.50
the:DT   0.00 20.50 20.00 20.00 20.50
latest:JJS 20.00 12.50 11.21 10.00 12.50
a:DT 10.00 20.50 20.00 20.00 20.50
day:NN 20.00   9.77 12.63   9.83 10.50
heartbreaking:VBG 20.00 15.50   9.25 11.94 15.50
reports:NNS 20.00   9.96 15.00 12.00 10.50
NO_WORD   1.00 10.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.97 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "boat" of "Estonia" dropped on aligned hyp word "Estonia"
-1.00  1.00 NullPunisher.other : set
-1.00  1.00 NullPunisher.other : sail
-2.00  1.00 RootEntailment.unalignedRoot : "set" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.1160
Threshold: -1.8794


Inference ID: 663

Txt: A remastered version of Paramount's "Breakfast at Tiffany's," starring Audrey Hepburn, is scheduled for release Nov. 2 at $40.

Hyp: Audrey Hepburn starred in "Breakfast at Tiffany's". (yes)

Audrey_Hepburn
NNP
starred
VBD
Breakfast
NNP
Tiffany
NNP
A:DT 20.50 20.00 20.00 20.50
remastered:VBN 15.50   6.17 15.00 15.50
version:NN 10.50 10.77   9.37 10.05
Paramount:NNP 10.00 15.50 10.07 10.21
Breakfast:NNP 10.50 15.00   0.00   9.90
Tiffany_'s:NNP 10.00 14.44   9.90   0.50
starring:VBG 15.50   0.00 15.00 15.50
Audrey_Hepburn:NNP   0.50 15.50 10.50 10.50
is:VBZ 15.50 10.00 15.00 15.50
scheduled:VBN 15.50 10.00 15.00 15.50
release:NN 10.50 13.97   9.25   9.67
Nov.:NNP 10.50 15.50   9.65   9.87
2:CD 20.50 20.50 20.50 20.50
$:$ 20.50 20.06 20.50 20.50
40:CD 20.50 20.50 20.50 20.50
NO_WORD 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.10 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-1.00  1.00 Structure.relMismatch : text "Audrey_Hepburn" is dobj of "starring" while hyp "Audrey_Hepburn" is nsubj of "starred" which aligned to text "starring"
Hand-tuned score (dot product of above): 1.4429
Threshold: -1.8794


Inference ID: 2081

Txt: The main race track in Qatar is located in Shahaniya, on the Dukhan Road.

Hyp: Qatar is located in Shahaniya. (don't know)

Qatar
NNP
is
VBZ
located
VBN
Shahaniya
NNP
The:DT 20.00 20.00 20.00 20.50
main:JJ 12.00 12.00   7.31 12.50
race:NN 10.00 15.00 14.97 10.50
track:NN 10.00 15.00 14.45 10.50
Qatar:NNP   0.50 15.50 15.50 10.00
is:VBZ 15.00   0.00 10.00 15.50
located:VBN 15.00 10.00   0.00 15.50
Shahaniya:NNP 10.50 15.50 15.50   0.00
the:DT 20.00 20.00 20.00 20.50
Dukhan_Road:NNP 10.50 15.50 15.50 10.00
NO_WORD 10.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.68 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Dukhan_Road" of "located" dropped on aligned hyp word "located"
-2.00  1.00 Location.mismatch : no clear info of matching: locate(X, prep_in)
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Qatar" <-prep_in-- "track" vs. hyp "Qatar" <-nsubjpass-- "located", which aligned to text "located"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.8849
Threshold: -1.8794


Inference ID: 648

Txt: Yoko Ono, widow of murdered Beatles star John Lennon, has plastered the small German town of Langenhagen with backsides.

Hyp: Yoko Ono was John Lennon's wife. (yes)

Yoko_Ono
NNP
was
VBD
John_Lennon
NNP
wife
NN
Yoko_Ono:NNP   0.00 15.50 10.00 10.50
widow:NN 10.50 15.00 10.50   2.62
murdered:NNP 10.50 15.00 10.50   6.11
Beatles:NNPS 10.50 15.00 10.50   9.65
star:NNP 10.50 15.00 10.50   7.83
John_Lennon:NNP 10.00 15.50   0.00 10.50
has:VBZ 15.50 10.00 15.50 15.00
plastered:VBN 15.50 10.00 15.50 14.04
the:DT 20.50 20.00 20.50 20.00
small:JJ 12.50 12.00 12.50 12.00
German:JJ 12.50 12.00 12.50 12.00
town:NN 10.50 15.00 10.50   7.61
Langenhagen:NNP 10.50 15.50 10.50 10.50
backsides:NNS 10.50 15.00 10.50   9.09
NO_WORD 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "star" of "John_Lennon" dropped on aligned hyp word "John_Lennon"
-0.05  1.00 NullPunisher.aux : was
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Yoko_Ono" <-nsubj-- "plastered" vs. hyp "Yoko_Ono" <-nsubj-- "wife", which aligned to text "widow" text "John_Lennon" is prep_of of "widow" while hyp "John_Lennon" is poss of "wife" which aligned to text "widow"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.5768
Threshold: -1.8794


Inference ID: 643

Txt: Practically every architect of international stature has vied for the bonanza of public and private projects -- including Renzo Piano, Peter Eisenman, Philip Johnson, Rafael Moneo, Helmut Jahn and Richard Rogers.

Hyp: Renzo Piano is an architect. (yes)

Renzo_Piano
NNP
is
VBZ
an
DT
architect
NN
Practically:RB 15.50 20.00 20.00 15.00
every:DT 20.50 20.00 10.00 20.00
architect:NN   8.42 15.00 20.00   0.00
international:JJ 12.50 12.00 20.00 10.76
stature:NN 10.20 15.00 20.00   8.76
has:VBZ 15.50   8.64 20.00 15.00
vied:VBN 15.50 10.00 20.00 15.00
the:DT 20.50 20.00 10.00 20.00
bonanza:NN   9.64 15.00 20.00   8.70
public:JJ 12.50 12.00 20.00 12.00
private:JJ 12.50 12.00 20.00 10.56
projects:NNS   8.37 15.00 20.00   6.63
including:VBG 15.50 10.00 20.00 15.00
Renzo_Piano:NNP   0.00 15.50 20.50   8.42
Peter_Eisenman:NNP   8.89 15.50 20.50   7.40
Philip_Johnson:NNP   9.19 15.50 20.50   7.85
Rafael_Moneo:NNP 10.50 15.50 20.50 10.50
Helmut_Jahn:NNP 10.50 15.50 20.50 10.50
Richard_Rogers:NNP   9.62 15.50 20.50   8.65
NO_WORD 10.00   1.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "stature" of "architect" dropped on aligned hyp word "architect"
-0.05  1.00 NullPunisher.aux : is
-0.10  1.00 NullPunisher.article : an
 1.00  1.00 Quant.contract : [every,an]
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Renzo_Piano" <-dobj-- "including" vs. hyp "Renzo_Piano" <-nsubj-- "architect", which aligned to text "architect" args have different parents, different relations: text "Renzo_Piano" <-pobj-- "including" vs. hyp "Renzo_Piano" <-nsubj-- "architect", which aligned to text "architect"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.8983
Threshold: -1.8794


Inference ID: 2068

Txt: There are more than 4,000 street children in the capital city of Ulan Bator.

Hyp: The population of Ulan Bator is 4,000. (don't know)

The
DT
population
NN
Ulan_Bator
NNP
is
VBZ
4,000
CD
There:EX 10.00 20.00 20.50 20.00 20.50
are:VBP 20.00 15.00 15.50   0.00 20.50
more_than:IN 20.00 20.00 20.50 20.00 20.50
4,000:CD 20.50 20.30 20.50 20.50   0.00
street_children:NNS 20.00   9.87 10.50 15.00 20.50
the:DT   0.00 20.00 20.50 20.00 20.50
capital:NN 20.00   8.27 10.50 15.00 20.50
city:NN 20.00   7.88 10.50 15.00 20.36
Ulan_Bator:NNP 20.50 10.50   0.50 15.50 20.50
NO_WORD   1.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.11 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "capital" of "city" dropped on aligned hyp word "population"
-0.05  1.00 NullPunisher.aux : is
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '4000.0' vs '>4000.0'
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "city" <-prep_in-- "street_children" vs. hyp "population" <-nsubj-- "4,000", which aligned to text "4,000"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -7.7877
Threshold: -1.8794


Inference ID: 2078

Txt: Twelve of Jupiter's moons are relatively small and seem to have been more likely captured than to have been formed in orbit around Jupiter.

Hyp: Jupiter has Twelve moons. (don't know)

Jupiter
NNP
has
VBZ
Twelve
CD
moons
NNS
Twelve:CD 20.50 20.50   0.00 20.50
Jupiter:NNP   0.00 15.50 20.50   4.55
moons:NNS   4.55 15.00 20.50   0.00
are:VBP 15.50   8.78 20.50 15.00
relatively:RB 15.50 20.00 20.50 13.97
small:JJ 12.50 12.00 20.50 12.00
seem:VBP 15.50 10.00 20.50 15.00
to:TO 20.50 20.00 20.50 20.00
have:VB 15.50   0.00 20.50 15.00
been:VBN 15.50 10.00 20.50 15.00
more:RBR 15.50 20.00 20.50 15.00
likely:RB 15.50 20.00 20.50 14.58
captured:VBN 15.50 10.00 20.50 15.00
than:IN 20.50 20.00 20.50 20.00
to:TO 20.50 20.00 20.50 20.00
have:VB 15.50   0.00 20.50 15.00
been:VBN 15.50 10.00 20.50 15.00
formed:VBN 15.50 10.00 20.50 15.00
orbit:NN   9.88 15.00 20.50   6.60
Jupiter:NNP   0.00 15.50 20.50   4.55
NO_WORD 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.37 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Jupiter" <-prep_around-- "formed" vs. hyp "Jupiter" <-nsubj-- "has", which aligned to text "have" args have different parents, different relations: text "moons" <-prep_of-- "Twelve" vs. hyp "moons" <-dobj-- "has", which aligned to text "have"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -2.2955
Threshold: -1.8794


Inference ID: 2072

Txt: In 1932 he joined the then illegal Communist party and held high government and party posts from 1942, becoming home secretary in 1948, when the Communist party took control in Hungary.

Hyp: Communist control in Hungary ended in 1948. (don't know)

Communist
JJ
control
NN
Hungary
NNP
ended
VBD
1948
CD
1932:CD 20.50 19.08 20.50 20.50   5.00
he:PRP 15.00 12.00 12.50 15.00 20.50
joined:VBD 12.00 13.40 15.50   8.44 16.82
the:DT 20.00 20.00 20.50 20.00 20.50
then:RB 12.00 15.00 15.50 20.00 20.50
illegal:JJ 10.00 10.75 12.50 12.00 20.50
Communist_party:NN   7.00   7.45 10.50 14.59 18.03
held:VBD 12.00 12.11 15.50   8.53 18.69
high:JJ 10.00 12.00 12.50 12.00 19.23
government:NN 12.00   7.08   8.58 15.00 20.50
party:NN 12.00   7.45   9.21 14.59 18.03
posts:NNS 12.00   7.34   7.92 15.00 19.82
1942:CD 20.50 20.17 20.50 19.51   3.36
becoming:VBG 12.00 15.00 15.50 10.00 20.50
home_secretary:NN 12.00 10.00 10.50 15.00 20.50
1948:CD 20.50 20.01 20.50 19.79   0.00
when:WRB 20.00 20.00 20.50 20.00 20.50
the:DT 20.00 20.00 20.50 20.00 20.50
Communist_party:NN   7.00   7.45 10.50 14.59 18.03
took_control:VBD 12.00 10.00 15.50 10.00 20.50
Hungary:NNP 12.50   9.27   0.00 15.50 20.50
NO_WORD   9.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.74 Alignment.score
 1.00  0.85 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "when" of "took_control" dropped on aligned hyp word "control"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1948
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "ended" aligned badly to "home_secretary"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "1948" <-prep_in-- "becoming" vs. hyp "1948" <-prep_in-- "ended", which aligned to text "home_secretary"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.3703
Threshold: -1.8794


Inference ID: 2066

Txt: The tallest building in Tokyo and the second tallest building in Japan, the TMGO was conceived as a high-tech center from which Tokyo could be led into the promising twenty-first century.

Hyp: TMGO is the tallest building in Japan. (don't know)

TMGO
NNP
is
VBZ
the
DT
tallest
JJS
building
NN
Japan
NNP
The:DT 20.50 20.00   0.00 20.00 20.00 20.50
tallest:JJS 12.50 12.00 20.00   0.00   6.69 12.50
building:NN 10.50 15.00 20.00   6.69   0.00   7.06
Tokyo:NNP 10.50 15.50 20.50 12.50   7.64   7.89
the:DT 20.50 20.00   0.00 20.00 20.00 20.50
second:JJ 12.50 12.00 20.00   9.34 12.00 12.50
tallest:JJS 12.50 12.00 20.00   0.00   6.69 12.50
building:NN 10.50 15.00 20.00   6.69   0.00   7.06
Japan:NNP 10.50 15.50 20.50 12.50   7.06   0.00
the:DT 20.50 20.00   0.00 20.00 20.00 20.50
TMGO:NNP   0.00 15.50 20.50 12.50 10.50 10.50
was:VBD 15.50   0.00 20.00 12.00 15.00 15.50
conceived:VBN 15.50 10.00 20.00 10.75 12.67 15.50
a:DT 20.50 20.00 10.00 20.00 20.00 20.50
high-tech:JJ 12.50 12.00 20.00 10.00 11.90 12.50
center:NN 10.50 15.00 20.00   8.40   5.78   7.25
from:IN 20.50 20.00 20.00 20.00 20.00 20.50
Tokyo:NNP 10.50 15.50 20.50 12.50   7.64   7.89
could:MD 20.50 18.76 10.00 20.00 20.00 20.50
be:VB 15.50   0.00 20.00 12.00 15.00 15.50
led:VBN 15.50 10.00 20.00 11.79 14.36 15.50
the:DT 20.50 20.00   0.00 20.00 20.00 20.50
promising:JJ 12.50 12.00 20.00 10.00 12.00 12.50
twenty-first:JJ 12.50 12.00 20.00 10.00 12.00 12.50
century:NN 10.50 15.00 20.00   8.68   7.72   9.12
NO_WORD 10.00   1.00   1.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.79 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
-5.00  1.00 Adjunct.superlative : text adjunct "second" of "building" dropped on aligned hyp word "building"
 0.00  1.00 NegPolarity.hypNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "TMGO" <-nsubjpass-- "conceived" vs. hyp "TMGO" <-nsubj-- "building", which aligned to text "building" args have different parents, different relations: text "tallest" <-ccomp-- "conceived" vs. hyp "tallest" <-amod-- "building", which aligned to text "building"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.00  1.00 NegPolarity.txtNegWord&NegPolarity.hypNegWord :
Hand-tuned score (dot product of above): -6.9505
Threshold: -1.8794


Inference ID: 2069

Txt: Grieving father Christopher Yavelow hopes to deliver one million letters to the queen of Holland to bring his children home.

Hyp: Christopher Yavelow is the queen of Holland. (don't know)

Christopher_Yavelow
NNP
is
VBZ
the
DT
queen
NN
Holland
NNP
Grieving:JJ 12.50 12.00 20.00 12.00 12.50
father:NN   8.79 15.00 20.00   5.46   9.42
Christopher_Yavelow:NNP   0.00 15.50 20.50   9.09   9.70
hopes:VBZ 15.50 10.00 20.00 15.00 15.50
to:TO 20.50 20.00 10.00 20.00 20.50
deliver:VB 15.50 10.00 20.00 15.00 15.50
one:CD 20.50 20.50 20.50 20.50 20.50
million:CD 20.50 20.50 20.50 19.81 20.50
letters:NNS   8.53 15.00 20.00   8.47   9.89
the:DT 20.50 20.00   0.00 20.00 20.50
queen:NN   9.09 15.00 20.00   0.00   9.85
Holland:NNP   9.70 15.50 20.50   9.85   0.00
to:TO 20.50 20.00 10.00 20.00 20.50
bring:VB 15.50 10.00 20.00 15.00 15.50
his:PRP$ 12.50 13.00 20.00 12.00 12.50
children:NNS   8.69 15.00 20.00   8.42   9.22
home:NN   8.91 15.00 20.00   8.80   7.77
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.81 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "father" of "Christopher_Yavelow" dropped on aligned hyp word "Christopher_Yavelow"
-1.00  1.00 Apposition.mismatch : no apposition in text between queen and Christopher_Yavelow
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Christopher_Yavelow" <-nsubj-- "hopes" vs. hyp "Christopher_Yavelow" <-nsubj-- "queen", which aligned to text "queen" args have different parents but same relations: text "Christopher_Yavelow" <-xsubj-- "deliver" vs. hyp "Christopher_Yavelow" <-nsubj-- "queen", which aligned to text "queen"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.0354
Threshold: -1.8794


Inference ID: 636

Txt: "How come a country (Vatican City), a so-called country, that is in essence 800 square acres of office space in the middle of Rome, that has a citizenry that excludes women and children, seems to attract the most attention in talking about public policy that deals with women and children?" she demanded.

Hyp: The Vatican City is in Rome. (yes)

The
DT
Vatican_City
NNP
is
VBZ
Rome
NNP
How:WRB 10.00 20.50 20.00 20.50
come:VBP 20.00 15.50 10.00 10.50
a:DT 10.00 20.50 20.00 20.50
country:NN 20.00 10.50 15.00   6.28
Vatican_City:NNP 20.50   0.00 15.50 10.00
a:DT 10.00 20.50 20.00 20.50
so-called:JJ 20.00 12.50 12.00 12.50
country:NN 20.00 10.50 15.00   6.28
that:WDT 10.00 20.50 20.00 20.50
is:VBZ 20.00 15.50   0.00 15.50
in_essence:IN 20.00 20.50 20.00 20.50
800:CD 20.50 20.50 20.50 20.50
square:JJ 20.00 12.50 12.00 12.50
acres:NNS 20.00 10.50 15.00   9.63
office:NN 20.00 10.50 15.00   9.22
space:NN 20.00 10.50 15.00   9.84
the:DT   0.00 20.50 20.00 20.50
middle:NN 20.00 10.50 15.00   4.30
Rome:NNP 20.50 10.00 15.50   0.00
that:WDT 10.00 20.50 20.00 20.50
has:VBZ 20.00 15.50   8.64 15.50
a:DT 10.00 20.50 20.00 20.50
citizenry:NN 20.00 10.50 15.00   9.45
that:WDT 10.00 20.50 20.00 20.50
excludes:VBZ 20.00 15.50 10.00 15.50
women:NNS 20.00 10.50 15.00   5.50
children:NNS 20.00 10.50 15.00   9.46
seems:VBZ 20.00 15.50 10.00 15.50
to:TO 10.00 20.50 20.00 20.50
attract:VB 20.00 15.50 10.00 15.50
the:DT   0.00 20.50 20.00 20.50
most:JJS 20.00 12.50 12.00 12.50
attention:NN 20.00 10.50 15.00   9.71
in:IN 20.00 20.50 20.00 20.50
talking:VBG 20.00 15.50 10.00 15.50
public:NN 20.00 10.50 15.00   9.54
policy:NN 20.00 10.50 15.00   9.39
that:WDT 10.00 20.50 20.00 20.50
deals:VBZ 20.00 15.50 10.00 15.50
women:NNS 20.00 10.50 15.00   5.50
children:NNS 20.00 10.50 15.00   9.46
she:PRP 20.00 12.50 15.00 12.50
demanded:VBD 20.00 15.50 10.00 15.50
NO_WORD   1.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.24 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-2.00  1.00 Location.mismatch : no clear info of matching: be(X, prep_in)
-0.10  1.00 NullPunisher.article : The
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Vatican_City" <-abbrev-- "country" vs. hyp "Vatican_City" <-nsubj-- "is", which aligned to text "is" args have different parents, different relations: text "Rome" <-prep_of-- "middle" vs. hyp "Rome" <-prep_in-- "is", which aligned to text "is"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.7279
Threshold: -1.8794


Inference ID: 347

Txt: Protest votes as citizens of the 25 EU nations punished their governments.

Hyp: The European Union is made up of 25 nations. (yes)

The
DT
European_Union
NNP
is
VBZ
made_up
VBN
25
CD
nations
NNS
Protest:JJ 20.00 12.50 12.00 12.00 20.50 12.00
votes:NNS 20.00   8.88 15.00 15.00 19.48   7.68
citizens:NNS 20.00   9.04 15.00 15.00 20.43   7.58
the:DT   0.00 20.50 20.00 20.00 20.50 20.00
25:CD 20.50 20.50 20.50 20.50   0.00 20.50
EU:NNP 20.50   0.00 15.50 15.50 20.50 10.50
nations:NNS 20.00   6.28 15.00 15.00 20.50   0.00
punished:VBN 20.00 15.50 10.00 10.00 20.11 15.00
their:PRP$ 20.00 12.50 15.00 15.00 20.50 12.00
governments:NNS 20.00   5.84 15.00 15.00 20.50   4.07
NO_WORD   1.00 10.00   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.07 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
-0.10  1.00 NullPunisher.article : The
-1.00  1.00 NullPunisher.other : made_up
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.unalignedRoot : "made_up" not aligned to anything
Hand-tuned score (dot product of above): -1.2435
Threshold: -1.8794


Inference ID: 988

Txt: Merrill Lynch & Co. and Smith Barney, now a unit of Citigroup, in 1998 settled discrimination cases involving hundreds of female employees.

Hyp: Merrill Lynch & Co. and Smith Barney are now a unit of Citigroup. (yes)

Merrill_Lynch_&_Co.
NNP
Smith_Barney
NNP
are
VBP
now
RB
a
DT
unit
NN
Citigroup
NNP
Merrill_Lynch_&_Co.:NNP   0.00   7.71 15.50 15.50 20.50   6.87 10.00
Smith_Barney:NNP   7.71   0.00 15.50 15.50 20.50   7.97 10.00
now:RB 15.50 15.50 20.00   0.00 20.00 15.00 15.50
a:DT 20.50 20.50 20.00 20.00   0.00 20.00 20.50
unit:NN   6.87   7.97 15.00 15.00 20.00   0.00 10.50
Citigroup:NNP 10.00 10.00 15.50 15.50 20.50 10.50   0.00
1998:CD 20.50 20.50 20.50 20.50 20.50 20.20 20.50
settled:VBD 15.50 15.50 10.00 20.00 20.00 15.00 15.50
discrimination:NN   9.06   9.66 15.00 15.00 20.00   7.94 10.50
cases:NNS   7.91   8.79 15.00 15.00 20.00   6.55 10.50
involving:VBG 15.50 15.50 10.00 20.00 20.00 14.57 15.50
hundreds:NNS   8.43   9.19 15.00 15.00 20.00   3.57 10.50
female:JJ 12.50 12.50 12.00 12.00 20.00 11.07 12.50
employees:NNS   7.23   7.59 15.00 15.00 20.00   6.43 10.50
NO_WORD 10.00 10.00   1.00   9.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.86 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.86 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : are
 1.00  1.00 Quant.contract : [a,a]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Merrill_Lynch_&_Co." <-nsubj-- "settled" vs. hyp "Merrill_Lynch_&_Co." <-nsubj-- "unit", which aligned to text "unit" args have different parents but same relations: text "now" <-advmod-- "Merrill_Lynch_&_Co." vs. hyp "now" <-advmod-- "unit", which aligned to text "unit"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -1.8629
Threshold: -1.8794


Inference ID: 2188

Txt: A second man, aged 24, technically freed yesterday was bailed after being questioned over alleged forged identity documents, but has been further detained on immigration matters, a spokesman for Scotland Yard said.

Hyp: A spokesman for Scotland Yard was freed yesterday. (don't know)

A
DT
spokesman
NN
Scotland_Yard
NNP
was
VBD
freed
VBN
yesterday
NN
A:DT   0.00 20.00 20.50 20.00 20.00 20.00
second:JJ 20.00 12.00 12.50 12.00 11.78 12.00
man:NN 20.00   5.75 10.50 15.00 12.05   6.86
aged:VBN 20.00 15.00 15.50 10.00   8.14 15.00
24:CD 20.50 20.50 20.50 20.50 20.50 20.26
technically:RB 20.00 14.25 15.50 20.00 19.21 15.00
freed:VBD 20.00 15.00 15.50 10.00   0.00 15.00
yesterday:NN 20.00   7.92 10.50 15.00 15.00   0.00
was:VBD 20.00 15.00 15.50   0.00 10.00 15.00
bailed:VBN 20.00 14.54 15.50 10.00   7.41 15.00
after:IN 20.00 20.00 20.50 20.00 20.00 20.00
being:VBG 20.00 15.00 15.50   0.00 10.00 15.00
questioned:VBN 20.00 12.66 15.50 10.00   9.43 13.65
alleged:JJ 20.00   9.66 12.50 12.00   9.48 10.42
forged:JJ 20.00 10.63 12.50 12.00 10.06   9.62
identity:NN 20.00   9.00 10.50 15.00 13.69   8.27
documents:NNS 20.00   6.13 10.50 15.00 14.29   5.26
has:VBZ 20.00 15.00 15.50 10.00 10.00 15.00
been:VBN 20.00 15.00 15.50   0.00 10.00 15.00
further:RBR 20.00 15.00 15.50 20.00 20.00 15.00
detained:VBN 20.00 13.30 15.50 10.00   5.16 15.00
immigration:NN 20.00   9.07 10.50 15.00 14.89   9.15
matters:NNS 20.00   5.84 10.50 15.00 14.58   6.14
a:DT   0.00 20.00 20.50 20.00 20.00 20.00
spokesman:NN 20.00   0.00 10.50 15.00 15.00   7.92
Scotland_Yard:NNP 20.50 10.50   0.00 15.50 15.50 10.50
said:VBD 20.00 14.96 15.50 10.00 10.00 13.61
NO_WORD   1.00 10.00 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.70 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "technically" of "freed" dropped on aligned hyp word "freed"
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: said-VBD
-0.05  1.00 NullPunisher.aux : was
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "spokesman" <-nsubj-- "said" vs. hyp "spokesman" <-nsubjpass-- "freed", which aligned to text "freed" args have different parents, different relations: text "yesterday" <-dep-- "bailed" vs. hyp "yesterday" <-tmod-- "freed", which aligned to text "freed"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.3352
Threshold: -1.8794


Inference ID: 2131

Txt: U.S. light crude was trading erratically on the New York Mercantile Exchange, hitting $44.50 a barrel before easing back a bit to $44.10, still a gain of $1.27.

Hyp: The U.S. light crude is $1.27 a barrel. (don't know)

The
DT
U.S.
NNP
light
NN
crude
NN
is
VBZ
$
$
1.27
CD
a
DT
barrel
NN
U.S.:NNP 20.50   0.00   8.31   8.53 15.50 20.50 20.50 20.50   9.02
light:NN 20.00   8.31   0.00   8.69 15.00 20.50 20.50 20.00   9.07
crude:NN 20.00   8.53   8.69   0.00 15.00 19.41 20.42 20.00   1.21
was:VBD 20.00 15.50 15.00 15.00   0.00 20.50 20.50 20.00 15.00
trading:VBG 20.00 15.50 13.78 14.59 10.00 19.60 20.50 20.00 14.88
erratically:RB 20.00 15.50 12.30 14.82 20.00 20.50 20.50 20.00 15.00
the:DT   0.00 20.50 20.00 20.00 20.00 10.50 20.50 10.00 20.00
New_York_Mercantile_Exchange:NNP 20.50 10.50 10.50 10.50 15.50 20.50 20.50 20.50 10.50
hitting:VBG 20.00 15.50 15.00 15.00 10.00 20.50 20.50 20.00 14.38
$:$ 10.50 20.50 20.50 19.41 20.50   0.00 15.59 10.50 18.57
44.50:CD 20.50 20.50 19.69 20.50 20.50 18.65   5.00 20.50 20.50
a:DT 10.00 20.50 20.00 20.00 20.00 10.50 20.50   0.00 20.00
barrel:NN 20.00   9.02   9.07   1.21 15.00 18.57 18.93 20.00   0.00
before:IN 20.00 20.50 20.00 20.00 20.00 20.50 20.50 18.78 20.00
easing:VBG 20.00 15.50 13.61 12.65 10.00 20.50 20.50 20.00 13.35
back:RB 20.00 15.50 13.54 15.00 20.00 20.42 20.50 20.00 15.00
a:DT 10.00 20.50 20.00 20.00 20.00 10.50 20.50   0.00 20.00
bit:RB 20.00 15.50 11.88 14.44 20.00 20.50 20.50 20.00 14.62
$:$ 10.50 20.50 20.50 19.41 20.50   0.00 15.59 10.50 18.57
44.10:CD 20.50 20.50 20.50 20.50 20.50 20.00   5.00 20.50 20.50
still:RB 20.00 15.50 15.00 15.00 20.00 20.50 20.50 20.00 15.00
a:DT 10.00 20.50 20.00 20.00 20.00 10.50 20.50   0.00 20.00
gain:NN 20.00   7.67   8.00   8.21 15.00 20.50 19.22 20.00   8.67
$:$ 10.50 20.50 20.50 19.41 20.50   0.00 15.59 10.50 18.57
1.27:CD 20.50 20.50 20.50 20.42 20.50 15.59   0.00 20.50 18.93
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.69 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-0.10  1.00 NullPunisher.article : The
-0.05  1.00 NullPunisher.aux : is
 1.00  1.00 Quant.contract : [a,a]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "crude" <-nsubj-- "trading" vs. hyp "crude" <-nsubj-- "$", which aligned to text "$" args have different parents, different relations: text "barrel" <-dobj-- "hitting" vs. hyp "barrel" <-dep-- "$", which aligned to text "$"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -2.2956
Threshold: -1.8794


Inference ID: 925

Txt: A rocket attack killed two Israelis, including a 3-year-old boy.

Hyp: A three-year-old was killed in a rocket attack. (yes)

A
DT
three-year-old
JJ
was
VBD
killed
VBN
a
DT
rocket
NN
attack
NN
A:DT   0.00 20.00 20.00 20.00   0.00 20.00 20.00
rocket:NN 20.00 12.00 15.00 13.51 20.00   0.00   6.01
attack:NN 20.00 12.00 15.00   9.17 20.00   6.01   0.00
killed:VBD 20.00 11.83 10.00   0.00 20.00 13.51   9.17
two:CD 20.50 15.82 20.50 19.32 20.50 20.50 20.07
Israelis:NNPS 20.00 12.00 15.00 15.00 20.00   9.10   9.02
including:VBG 20.00 11.41 10.00   7.37 20.00 15.00 12.82
a:DT   0.00 20.00 20.00 20.00   0.00 20.00 20.00
3-year-old:JJ 20.00   0.00 12.00 12.00 20.00 11.85 12.00
boy:NN 20.00 11.50 15.00 13.25 20.00   8.74   8.64
NO_WORD   1.00   9.00   1.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.53 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.57 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : was
-0.10  1.00 NullPunisher.article : A
 1.00  1.00 Quant.contract : [a,a]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "3-year-old" <-dobj-- "including vs. hyp "three-year-old" <-nsubjpass-- "killed", which aligned to text "killed" args have different parents, different relations: text "3-year-old" <-pobj-- "including" vs. hyp "three-year-old" <-nsubjpass-- "killed", which aligned to text "killed" text "attack" is nsubj of "killed" while hyp "attack" is prep_in of "killed" which aligned to text "killed"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -2.4341
Threshold: -1.8794


Inference ID: 245

Txt: It found that binge drinking in women aged 18 to 44 increased in the United States by 13 percent between 1999 and 2002.

Hyp: Female binge drinking rose 13 percent in three years. (yes)

Female
JJ
binge
NN
drinking
NN
rose
VBD
13
CD
percent
NN
three
CD
years
NNS
It:PRP 15.00 12.00 12.00 15.00 20.50 12.50 20.50 12.00
found:VBD 12.00 11.86 10.99   8.84 20.12 15.50 19.99 13.58
that:IN 20.00 20.00 20.00 20.00 20.50 20.50 20.50 20.00
binge:NN 12.00   0.00   8.26 15.00 19.66   9.81 19.59   8.86
drinking:NN 12.00   8.26   0.00 14.70 19.32   9.76 19.68   8.79
women:NNS 12.00   8.02   8.34 15.00 20.24   8.56 19.88   7.27
aged:VBN 12.00 12.34 12.18   9.48 20.30 14.14 19.56 11.40
18:CD 20.50 19.31 18.33 19.73   2.65 20.48   4.31 20.48
to:TO 20.00 20.00 20.00 20.00 20.50 20.50 20.50 20.00
44:CD 20.50 20.14 19.72 17.98   6.83 20.50   5.00 20.50
increased:VBD 12.00 12.76 12.96   5.20 19.45 13.31 20.32 14.36
the:DT 20.00 20.00 20.00 20.00 20.50 20.50 20.50 20.00
United_States:NNP 12.50   9.20   9.12 15.50 20.50   8.25 20.50   7.38
13:CD 20.50 19.66 19.32 19.95   0.00 20.00   7.30 20.50
percent:NN 12.50   9.81   9.76 12.14 20.00   0.00 20.50   7.73
1999:CD 20.50 20.50 20.50 20.50   8.62 20.50 10.50 20.50
2002:CD 20.50 20.50 20.49 20.50   8.04 20.50 10.50 20.50
NO_WORD   9.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.97 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added years[years-NNS]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "1999" of "percent" dropped on aligned hyp word "percent"
-1.00  1.00 NullPunisher.other : years
-1.00  1.00 NullPunisher.other : Female
-1.00  1.00 NullPunisher.other : rose
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '3.0' vs '44.0'
-2.00  1.00 RootEntailment.unalignedRoot : "rose" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -9.1279
Threshold: -1.8794


Inference ID: 2182

Txt: Two bombs planted near an Islamic school in Pakistan killed eight people and injured 42 others yesterday in the latest outbreak of violence gripping the southern port of Karachi.

Hyp: Eight people planted two bombs near an Islamic school in Pakistan. (don't know)

Eight
CD
people
NNS
planted
VBD
two
CD
bombs
NNS
an
DT
Islamic
JJ
school
NN
Pakistan
NNP
Two:CD   5.00 20.50 20.50   0.00 20.50 20.50 20.50 20.50 20.50
bombs:NNS 20.50   6.80 12.23 19.56   0.00 20.00 12.00   8.17   9.84
planted:VBN 20.50 14.27   0.00 19.77 12.23 20.00 12.00 14.49 15.50
an:DT 20.50 20.00 20.00 20.50 20.00   0.00 20.00 20.00 20.50
Islamic:JJ 20.50 12.00 12.00 20.50 12.00 20.00   0.00 12.00 12.50
school:NN 20.50   5.48 14.49 20.50   8.17 20.00 12.00   0.00   9.66
Pakistan:NNP 20.50   8.75 15.50 20.50   9.84 20.50 12.50   9.66   0.00
killed:VBD 20.50 11.02   7.39 19.32   9.04 20.00 12.00 15.00 15.50
eight:CD   0.00 20.50 20.50   1.72 18.87 20.50 20.50 20.50 20.50
people:NNS 20.50   0.00 14.27 20.50   6.80 20.00 12.00   5.48   8.75
injured:VBD 20.50 12.35   9.04 19.77 11.39 20.00 12.00 15.00 15.50
42:CD   5.00 20.50 20.12   5.00 20.50 20.50 20.50 19.83 20.50
others:NNS 20.50   7.74 15.00 20.50 10.00 20.00 12.00 10.00 10.50
yesterday:NN 20.50   6.54 14.99 20.50   8.76 20.00 12.00   7.98   9.45
the:DT 20.50 20.00 20.00 20.50 20.00 10.00 20.00 20.00 20.50
latest:JJS 20.50 12.00 11.70 19.75 12.00 20.00 10.00 12.00 12.50
outbreak:NN 20.50   7.95 13.06 19.78   5.06 20.00 12.00   9.18 10.20
violence:NN 20.50   7.02 13.41 20.23   7.27 20.00 12.00   8.23   9.68
gripping:VBG 20.50 15.00   8.49 20.34 14.48 20.00 12.00 15.00 15.50
the:DT 20.50 20.00 20.00 20.50 20.00 10.00 20.00 20.00 20.50
southern:JJ 20.50 12.00 10.73 20.39 11.10 20.00   9.36 10.46 12.50
port:NN 20.50   6.48 12.83 20.50   7.48 20.00 12.00   7.93   7.85
Karachi:NNP 20.50 10.50 15.50 20.50 10.50 20.50 12.50 10.50 10.00
NO_WORD 10.00 10.00 10.00 10.00 10.00   1.00   9.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.82 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 1.00  1.00 Quant.contract : [an,an]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Pakistan" <-prep_in-- "school" vs. hyp "Pakistan" <-prep_in-- "planted", which aligned to text "planted" args have different parents, different relations: text "people" <-dobj-- "killed" vs. hyp "people" <-nsubj-- "planted", which aligned to text "planted" args have different parents, different relations: text "bombs" <-nsubj-- "killed" vs. hyp "bombs" <-dobj-- "planted", which aligned to text "planted"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -1.2356
Threshold: -1.8794


Inference ID: 976

Txt: Harvey Weinstein, the co-chairman of Miramax, who was instrumental in popularizing both independent and foreign films with broad audiences, agrees.

Hyp: Harvey Weinstein is the co-chairman of Miramax. (yes)

Harvey_Weinstein
NNP
is
VBZ
the
DT
co-chairman
NN
Miramax
NNP
Harvey_Weinstein:NNP   0.00 15.50 20.50   9.29 10.50
the:DT 20.50 20.00   0.00 20.00 20.50
co-chairman:NN   9.29 15.00 20.00   0.00 10.50
Miramax:NNP 10.50 15.50 20.50 10.50   0.00
who:WP 12.50 15.00 20.00 12.00 12.50
was:VBD 15.50   0.00 20.00 15.00 15.50
instrumental:JJ 12.50 12.00 20.00 12.00 12.50
in:IN 20.50 20.00 20.00 20.00 20.50
popularizing:VBG 15.50 10.00 20.00 15.00 15.50
both:DT 20.50 20.00 10.00 20.00 20.50
independent:JJ 12.50 12.00 20.00 12.00 12.50
foreign:JJ 12.50 12.00 20.00 12.00 12.50
films:NNS   9.80 15.00 20.00   7.66 10.50
broad:JJ 12.50 12.00 20.00 12.00 12.50
audiences:NNS   9.71 15.00 20.00   7.47 10.50
agrees:VBZ 15.50 10.00 20.00 15.00 15.50
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.96 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "instrumental" of "Miramax" dropped on aligned hyp word "Miramax"
-0.05  1.00 NullPunisher.aux : is
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Harvey_Weinstein" <-nsubj-- "agrees" vs. hyp "Harvey_Weinstein" <-nsubj-- "co-chairman", which aligned to text "co-chairman" args have different parents, different relations: text "Miramax" <-nsubj-- "instrumental" vs. hyp "Miramax" <-prep_of-- "co-chairman", which aligned to text "co-chairman"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.8603
Threshold: -1.8794


Inference ID: 242

Txt: On opening day Salesforce.com's stock was up $6.20 to $17.20, a 56.4% increase over the initial offering price of $11.

Hyp: Opening day of the stock of Salesforce.com rose 56.4% over the initial offering price. (yes)

Opening
NN
day
NN
the
DT
stock
NN
Salesforce.com
NNP
rose
VBD
56.4
CD
%
NN
the
DT
initial_offering
NN
price
NN
opening:NN   0.00   5.40 20.00   7.53 10.50 15.00 19.58 10.31 20.00   9.08   7.45
day:NN   6.69   0.00 20.00   6.95 10.50 13.28 20.50 10.32 20.00   8.75   6.86
Salesforce.com:NNP 10.50 10.50 20.50 10.50   0.00 15.50 20.50 10.50 20.50 10.50 10.50
stock:NN   7.53   6.95 20.00   0.00 10.50 14.26 19.46 10.01 20.00   8.76   4.56
was:VBD 15.00 15.00 20.00 15.00 15.50 10.00 20.50 15.50 20.00 15.00 15.00
up:RB 15.00 15.00 20.00 15.00 15.50 20.00 20.50 15.50 20.00 15.00 15.00
$:$ 20.50 19.80 10.50 18.84 20.50 20.16 19.82 19.30 10.50 20.50 19.13
6.20:CD 20.50 20.50 20.50 20.50 20.50 18.53   9.80 19.40 20.50 20.50 19.70
$:$ 20.50 19.80 10.50 18.84 20.50 20.16 19.82 19.30 10.50 20.50 19.13
17.20:CD 20.50 20.50 20.50 17.50 20.50 19.81 10.50 19.34 20.50 20.50 17.39
a:DT 20.00 20.00 10.00 20.00 20.50 20.00 20.50 20.50 10.00 20.00 20.00
56.4:CD 20.50 20.50 20.50 19.46 20.50 17.57   0.00 20.00 20.50 20.50 20.50
%:NN 10.50 10.32 20.50 10.01 10.50 15.50 20.00   0.00 20.50 10.50   9.38
increase:NN   6.93   5.11 20.00   7.18 10.50 12.09 18.85   9.38 20.00   8.88   6.95
the:DT 20.00 20.00   0.00 20.00 20.50 20.00 20.50 20.50   0.00 20.00 20.00
initial_offering:NN   9.08   8.75 20.00   8.76 10.50 15.00 20.50 10.50 20.00   0.00   8.70
price:NN   7.45   6.86 20.00   4.56 10.50 14.80 20.50   9.38 20.00   8.70   0.00
$:$ 20.50 19.80 10.50 18.84 20.50 20.16 19.82 19.30 10.50 20.50 19.13
11:CD 20.50 19.56 20.50 20.50 20.50 19.87   9.47 20.50 20.50 20.50 20.50
NO_WORD 10.00 10.00   1.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.66 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.36 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "$" of "price" dropped on aligned hyp word "price"
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : rose
-2.00  1.00 RootEntailment.unalignedRoot : "rose" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.8306
Threshold: -1.8794


Inference ID: 198

Txt: Even though the rug was ripped out from under Lollapalooza this week, Jane's Addiction frontman Perry Farrell is optimistic that the eclectic music festival he co-founded in 1991 will resurface and it might be sooner than you think.

Hyp: Perry Farrel hopes to found another music festival. (don't know)

Perry_Farrel
NNP
hopes
VBZ
to
TO
found
VBD
another
DT
music
NN
festival
NN
Even:RB 15.50 20.00 20.00 20.00 20.00 15.00 15.00
though:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.50 20.00 10.00 20.00 10.00 20.00 20.00
rug:NN   9.68 14.63 20.00 13.86 20.00   8.09   9.58
was:VBD 15.50 10.00 20.00 10.00 20.00 15.00 15.00
ripped_out:VBN 15.50 10.00 20.00 10.00 20.00 15.00 15.00
from:IN 20.50 20.00 17.78 20.00 20.00 20.00 20.00
Lollapalooza:NNP 10.50 15.50 20.50 15.50 20.50 10.50 10.50
this:DT 20.50 20.00 10.00 20.00 10.00 20.00 20.00
week:NN   9.60 13.90 20.00 14.35 20.00   5.67   6.51
Jane:NNP 10.00 15.50 20.50 15.50 20.50 10.50 10.50
Addiction:NNP 10.27 15.00 20.00 15.00 20.00   8.37   9.69
frontman:NNP 10.50 13.20 20.00 15.00 20.00   9.61 10.00
Perry_Farrell:NNP   0.00 15.50 20.50 15.50 20.50   9.18 10.30
is:VBZ 15.50 10.00 20.00 10.00 20.00 15.00 15.00
optimistic:JJ 12.50   8.82 20.00 12.00 20.00 12.00 10.99
that:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00
the:DT 20.50 20.00 10.00 20.00 10.00 20.00 20.00
eclectic:JJ 12.50 10.93 20.00 11.78 20.00   7.01   6.61
music:NN   9.18 15.00 20.00 15.00 20.00   0.00   3.48
festival:NN 10.30 13.68 20.00 15.00 20.00   3.48   0.00
he:PRP 12.50 15.00 20.00 15.00 20.00 12.00 12.00
co-founded:VBD 15.50 10.00 20.00   5.00 20.00 15.00 15.00
1991:CD 20.50 20.50 20.50 17.62 20.50 20.42 19.61
will:MD 20.50 20.00 10.00 20.00 10.00 20.00 20.00
resurface:VB 15.50   9.23 20.00 10.00 20.00 14.10 13.26
it:PRP 12.50 15.00 20.00 15.00 20.00 12.00 12.00
might:MD 20.50 20.00 10.00 20.00 10.00 20.00 20.00
be:VB 15.50 10.00 20.00 10.00 20.00 15.00 15.00
sooner:RBR 15.50 16.66 20.00 20.00 20.00 15.00 14.68
than:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00
you:PRP 12.50 15.00 20.00 15.00 20.00 12.00 12.00
think:VBP 15.50 10.00 20.00 10.00 20.00 14.81 15.00
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.41 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.43 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "eclectic" of "festival" dropped on aligned hyp word "festival"
-1.00  1.00 NullPunisher.other : hopes
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : another
-2.00  1.00 RootEntailment.unalignedRoot : "hopes" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.8259
Threshold: -1.8794


Inference ID: 345

Txt: Ahern, who was travelling to Tokyo for an EU-Japan summit yesterday, will consult with other EU leaders by telephone later this week in an effort to find an agreed candidate.

Hyp: A summit between Europe and Japan is taking place in the Japanise capital. (yes)

A
DT
summit
NN
Europe
NNP
Japan
NNP
is
VBZ
taking
VBG
place
NN
the
DT
Japanise
NNP
capital
NN
Ahern:NNP 20.50 10.50 10.50 10.50 15.50 15.50 10.50 20.50 10.50 10.50
who:WP 20.00 12.00 12.50 12.50 15.00 15.00 12.00 20.00 12.50 12.00
was:VBD 20.00 15.00 15.50 15.50   0.00 10.00 15.00 20.00 15.50 15.00
traveling:VBG 20.00 11.72 15.50 15.50 10.00   8.62 13.57 20.00 15.50 15.00
Tokyo:NNP 20.50   9.19   8.14   7.89 15.50 15.50   6.62 20.50 10.00   8.28
an:DT 10.00 20.00 20.50 20.50 20.00 20.00 20.00 10.00 20.50 20.00
EU-Japan:JJ 20.00 12.00 12.50   7.50 12.00 12.00 12.00 20.00   7.50 12.00
summit:NN 20.00   0.00   9.13   8.93 15.00 15.00   8.08 20.00 10.50   7.90
yesterday:NN 20.00   8.26   8.59   8.35 15.00 15.00   7.43 20.00 10.50   7.21
will:MD 10.00 20.00 20.50 20.50 20.00 20.00 20.00 10.00 20.50 20.00
consult:VB 20.00 12.12 15.50 15.50 10.00   9.71 14.37 20.00 15.50 15.00
other:JJ 20.00 12.00 12.50 12.50 12.00 12.00 12.00 20.00 12.50 12.00
EU:NNP 20.50   9.66   8.52   8.49 15.50 15.50 10.50 20.50 10.00 10.50
leaders:NNS 20.00   6.28   8.98   8.76 15.00 15.00   7.89 20.00 10.50   7.69
telephone:NN 20.00   8.50   8.28   8.01 15.00 15.00   7.22 20.00 10.50   7.53
later:RBR 20.00 13.23 15.50 15.50 20.00 18.39 13.71 20.00 15.50 15.00
this:DT 10.00 20.00 20.50 20.50 20.00 20.00 20.00 10.00 20.50 20.00
week:NN 20.00   8.17   8.49   8.24 15.00 14.88   7.31 20.00 10.50   7.08
an:DT 10.00 20.00 20.50 20.50 20.00 20.00 20.00 10.00 20.50 20.00
effort:NN 20.00   7.55   7.84   7.54 15.00 10.99   6.54 20.00 10.50   6.27
to:TO 10.00 20.00 20.50 20.50 20.00 20.00 20.00 10.00 20.50 20.00
find:VB 20.00 15.00 15.50 15.50 10.00   6.16 10.60 20.00 15.50 14.44
an:DT 10.00 20.00 20.50 20.50 20.00 20.00 20.00 10.00 20.50 20.00
agreed:JJ 20.00   9.80 12.50 12.50 12.00 10.68 12.00 20.00 12.50   9.91
candidate:NN 20.00   8.87   8.74   8.51 15.00 14.80   7.75 20.00 10.50   8.02
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.44 Alignment.score
 1.00  0.81 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  9.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.10 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added capital[capital-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "EU-Japan" of "summit" dropped on aligned hyp word "summit"
-0.10  1.00 NullPunisher.article : A
-3.00  1.00 NullPunisher.entity : Europe
-1.00  1.00 NullPunisher.other : taking
-3.00  1.00 NullPunisher.entity : Japan
-3.00  1.00 NullPunisher.entity : Japanise
-1.00  1.00 NullPunisher.other : place
-0.10  1.00 NullPunisher.article : the
-1.00  1.00 NullPunisher.other : capital
-0.05  1.00 NullPunisher.aux : is
 1.00  1.00 Quant.contract : [an,a]
-2.00  1.00 RootEntailment.unalignedRoot : "taking" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -12.7913
Threshold: -1.8794


Inference ID: 930

Txt: After giving nearly 5,000 people a second chance at life, doctors are celebrating the 25th anniversary of Britian's first heart transplant which was performed at Cambridgeshire's Papworth Hospital in 1979.

Hyp: The first heart transplant in Britian was performed in 1979. (yes)

The
DT
first
JJ
heart
NN
transplant
NN
Britian
NNP
was
VBD
performed
VBN
1979
CD
After:IN 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50
giving:VBG 20.00 12.00 15.00 15.00 15.50 10.00 10.00 20.18
nearly:RB 20.00 12.00 14.97 15.00 15.50 20.00 18.23 17.32
5,000:CD 20.50 20.50 20.50 20.44 20.50 20.50 19.13   9.54
people:NNS 20.00 12.00   7.62   8.13 10.50 15.00 15.00 20.50
a:DT 10.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50
second:JJ 20.00   8.12 12.00 12.00 12.50 12.00 10.05 18.54
chance:NN 20.00 12.00   8.21   8.64 10.50 15.00 15.00 19.79
life:NN 20.00 12.00   5.70   6.96 10.50 15.00 14.06 19.14
doctors:NNS 20.00 12.00   3.14   2.94 10.50 15.00 13.29 20.50
are:VBP 20.00 12.00 15.00 15.00 15.50   0.00 10.00 20.50
celebrating:VBG 20.00 12.00 13.77 14.25 15.50 10.00   6.88 19.57
the:DT   0.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50
25th:JJ 20.00 10.00 12.00 12.00 12.50 12.00   9.59 19.49
anniversary:NN 20.00 12.00   9.24   9.12 10.50 15.00 12.40 19.02
Britian:NNP 20.50 12.50 10.50 10.50   0.00 15.50 15.50 20.50
first:JJ 20.00   0.00 12.00 12.00 12.50 12.00 12.00 20.50
heart:NN 20.00 12.00   0.00   2.12 10.50 15.00 13.46 20.50
transplant:NN 20.00 12.00   2.12   0.00 10.50 15.00 12.48 20.50
which:WDT 10.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50
was:VBD 20.00 12.00 15.00 15.00 15.50   0.00 10.00 20.50
performed:VBN 20.00 12.00 13.46 12.48 15.50 10.00   0.00 18.19
Cambridgeshire:NNP 20.50 12.50 10.50 10.50 10.50 15.50 15.50 20.50
Papworth_Hospital:NNP 20.50 12.50   9.28   9.31 10.00 15.50 15.50 20.50
1979:CD 20.50 20.50 20.50 20.50 20.50 20.50 18.19   0.00
NO_WORD   1.00   9.00 10.00 10.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.80 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  7.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Papworth_Hospital" of "performed" dropped on aligned hyp word "performed"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1979
-0.10  1.00 NullPunisher.article : The
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "1979" <-prep_in-- "Papworth_Hospital" vs. hyp "1979" <-prep_in-- "performed", which aligned to text "performed" args have different parents, different relations: text "transplant" <-prep_of-- "anniversary" vs. hyp "transplant" <-nsubjpass-- "performed", which aligned to text "performed"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.1837
Threshold: -1.8794


Inference ID: 927

Txt: More than 150 dolphins, marine turtles and beaked whales have been washed up dead on beaches in Africa.

Hyp: Dead dolphins, turtles and whales have been found on African beaches. (yes)

Dead
NNP
dolphins
NNS
turtles
NNS
whales
NNS
have
VBP
been
VBN
found
VBN
African
JJ
beaches
NNS
More_than:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
150:CD 20.50 20.50 20.48 20.50 20.50 20.50 20.50 20.50 20.11
dolphins:NNS   9.90   0.00   6.00   8.45 15.00 15.00 15.00 12.00   9.18
marine_turtles:NNS   9.82   6.08   0.20   8.45 15.00 15.00 15.00 12.00   9.13
beaked_whales:NNS 10.00 10.00   4.31   5.00 15.00 15.00 15.00 12.00 10.00
have:VBP 15.00 15.00 15.00 15.00   0.00 10.00   7.71 12.00 15.00
been:VBN 15.00 15.00 15.00 15.00 10.00   0.00 10.00 12.00 15.00
washed:VBN 15.00 14.49 12.41 11.79 10.00 10.00   7.35 12.00 10.88
up:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
dead:JJ   2.00 11.82 10.53   8.88 12.00 12.00   8.84 10.00   9.99
beaches:NNS   9.66   9.18   5.36   4.62 15.00 15.00 13.58 12.00   0.00
Africa:NNP 10.14   9.67   9.60   9.08 15.50 15.50 15.50   5.50   9.32
NO_WORD 10.00 10.00 10.00 10.00   1.00   1.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.17 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "150" of "dolphins" dropped on aligned hyp word "dolphins"
 1.00  1.00 Hypernym.posWiden : widening in positive context: marine_turtles -> turtle
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "found" aligned badly to "washed"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.0719
Threshold: -1.8794


Inference ID: 1001

Txt: The so-called "grandmother hypothesis", based on studies of African hunter-gatherer groups, suggests that infertile women are vital for successful child-rearing despite being unable to produce children themselves.

Hyp: The "grandmother hypothesis" suggests that infertile women are very important for raising children. (yes)

The
DT
grandmother
NN
hypothesis
NN
suggests
VBZ
that
IN
infertile
JJ
women
NNS
are
VBP
very
RB
important
JJ
for
IN
raising
VBG
children
NNS
The:DT   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
so-called:JJ 20.00 12.00 12.00 12.00 20.00 10.00 12.00 12.00 12.00 10.00 20.00 12.00 12.00
grandmother:NN 20.00   0.00 10.00 15.00 20.00 11.28   8.16 15.00 15.00 12.00 20.00 14.66   4.81
hypothesis:NN 20.00 10.00   0.00 11.90 20.00   9.67   8.87 15.00 15.00 10.98 20.00 15.00 10.00
based:VBN 20.00 15.00 12.90   8.07 20.00 12.00 15.00 10.00 20.00 11.17 20.00   9.51 15.00
studies:NNS 20.00   9.29   4.55 10.61 20.00   8.27   6.69 15.00 15.00 10.45 20.00 14.19   8.31
African:NNP 20.50   9.00 10.50 15.50 20.50 12.50   8.04 15.50 15.50 12.50 20.50 15.50   8.60
hunter-gatherer:JJ 20.00 11.13 10.45 12.00 20.00   9.14 11.33 12.00 12.00 10.00 20.00 11.50 12.00
groups:NNS 20.00   7.72   9.02 14.18 20.00 10.57   5.31 15.00 15.00 10.86 20.00 13.99   6.15
suggests:VBZ 20.00 15.00 11.90   0.00 20.00 11.24 13.10 10.00 20.00   8.76 20.00   6.91 13.92
that:IN 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00
infertile:JJ 20.00 11.28   9.67 11.24 20.00   0.00   7.31 12.00 12.00   9.38 20.00 10.06 10.01
women:NNS 20.00   8.16   8.87 13.10 20.00   7.31   0.00 15.00 15.00 10.37 20.00 13.95   5.32
are:VBP 20.00 15.00 15.00 10.00 20.00 12.00 15.00   0.00 20.00 12.00 20.00 10.00 15.00
vital:JJ 20.00 12.00 11.75   8.90 20.00   9.88 11.53 12.00 12.00   3.92 20.00 11.56 11.82
for:IN 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00
successful:JJ 20.00 12.00 12.00 11.12 20.00 10.00 10.50 12.00 12.00   5.11 20.00 11.27 11.48
child-rearing:NN 20.00   4.49   9.85 14.14 20.00 10.09   6.76 15.00 15.00 11.63 20.00 13.48   0.00
despite:IN 20.00 19.98 20.00 16.64 10.00 20.00 19.77 20.00 20.00 15.92   0.00 18.22 19.51
being:VBG 20.00 15.00 15.00 10.00 20.00 12.00 15.00   0.00 20.00 12.00 20.00 10.00 15.00
unable:JJ 20.00   8.40 12.00 12.00 20.00 10.00 11.09 12.00 12.00   9.14 20.00 10.04   8.77
to:TO 10.00 20.00 20.00 20.00 20.00 20.00 18.72 20.00 20.00 20.00 17.43 20.00 20.00
produce:VB 20.00 15.00 14.24   7.58 20.00 12.00 14.83 10.00 20.00 11.22 20.00   8.42 14.71
children:NNS 20.00   4.81 10.00 13.92 20.00 10.01   5.32 15.00 15.00 11.55 20.00 12.67   0.00
children:NNS 20.00   4.81 10.00 13.92 20.00 10.01   5.32 15.00 15.00 11.55 20.00 12.67   0.00
NO_WORD   1.00 10.00 10.00 10.00   1.00   9.00 10.00   1.00   9.00   9.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.31 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  8.00 Alignment.hypSpan
 0.10  0.46 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added very[very-RB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "studies" of "hypothesis" dropped on aligned hyp word "hypothesis"
-1.00  1.00 NullPunisher.other : very
-1.00  1.00 NullPunisher.other : for
-1.00  1.00 NullPunisher.other : raising
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.2301
Threshold: -1.8794


Inference ID: 2128

Txt: The kidnappers want the Kuwaiti firm that employs the men to stop doing business in Iraq and to pay compensation to the victims of U.S. strikes in Falluja.

Hyp: The kidnappers paid compensation to the victims of the U.S. (don't know)

The
DT
kidnappers
NNS
paid
VBD
compensation
NN
the
DT
victims
NNS
the
DT
U.S.
NNP
The:DT   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
kidnappers:NNS 20.00   0.00 12.83   9.20 20.00   4.74 20.00   9.46
want:VBP 20.00 14.56   9.28 15.00 20.00 14.84 20.00 15.50
the:DT   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
Kuwaiti:NNP 20.50 10.50 15.50 10.50 20.50 10.50 20.50 10.00
firm:NN 20.00   8.76 14.48   7.09 20.00   7.25 20.00   4.72
that:WDT 10.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50
employs:VBZ 20.00 14.89   6.96 11.73 20.00 14.47 20.00 15.50
the:DT   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
men:NNS 20.00   8.03 15.00   7.10 20.00   6.18 20.00   4.74
to:TO 10.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50
stop:VB 20.00 14.44 10.00 15.00 20.00 14.81 20.00 15.50
doing:VBG 20.00 15.00   9.00 14.26 20.00 15.00 20.00 15.50
business:NN 20.00   8.55 14.01   6.76 20.00   6.93 20.00   4.23
Iraq:NNP 20.50   9.91 15.50   9.68 20.50   9.48 20.50   8.47
to:TO 10.00 20.00 20.00 20.00 10.00 20.00 10.00 20.50
pay:VB 20.00 13.06   0.00   9.87 20.00 13.72 20.00 15.50
compensation:NN 20.00   9.20 10.17   0.00 20.00   7.39 20.00   7.93
the:DT   0.00 20.00 20.00 20.00   0.00 20.00   0.00 20.50
victims:NNS 20.00   4.74 14.39   7.39 20.00   0.00 20.00   8.08
U.S.:NNP 20.50   9.46 15.50   7.93 20.50   8.08 20.50   0.00
strikes:NNS 20.00   8.43 13.54   7.23 20.00   8.54 20.00   8.56
Falluja:NNP 20.50 10.50 15.50 10.50 20.50 10.50 20.50 10.00
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.58 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.62 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "strikes" of "victims" dropped on aligned hyp word "victims"
-0.10  1.00 NullPunisher.article : the
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "kidnappers" <-nsubj-- "want" vs. hyp "kidnappers" <-nsubj-- "paid", which aligned to text "pay"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.0260
Threshold: -1.8794


Inference ID: 348

Txt: Protest votes as citizens of the 25 EU nations punished their governments for everything from high unemployment to involvement in Iraq.

Hyp: EU nations' citizens protest against punishment for unemployment. (don't know)

EU
NNP
nations
NNS
citizens
NNS
protest
VB
punishment
NN
unemployment
NN
Protest:JJ 12.50 12.00 12.00   2.00 12.00 12.00
votes:NNS 10.50   7.68   8.54 10.00   7.48   8.90
citizens:NNS 10.50   7.58   0.00 11.49   8.28   9.03
the:DT 20.50 20.00 20.00 20.00 20.00 20.00
25:CD 20.50 20.50 20.43 20.50 20.27 19.90
EU:NNP   0.50 10.50 10.50 15.50 10.50 10.50
nations:NNS 10.50   0.00   7.58 14.73   7.69   8.33
punished:VBN 15.50 15.00 12.70   8.40   1.00 15.00
their:PRP$ 12.50 12.00 12.00 15.00 12.00 12.00
governments:NNS 10.50   4.07   5.29 13.65   7.38   8.08
everything:NN 10.50 10.00 10.00 15.00 10.00 10.00
high:JJ 12.50 12.00 12.00 10.34 10.28 12.00
unemployment:NN 10.50   8.33   9.03 13.68   8.91   0.00
involvement:NN 10.50   7.84   7.26 12.98   6.54   9.00
Iraq:NNP   8.72   9.39   9.68 15.50   9.83 10.12
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.97 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "high" of "unemployment" dropped on aligned hyp word "unemployment"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "citizens" <-prep_as-- "votes" vs. hyp "citizens" <-nsubj-- "protest", which aligned to text "Protest"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.4257
Threshold: -1.8794


Inference ID: 194

Txt: Wal-Mart has received a lot of negative publicity recently, including allegations that it used illegal workers and made employees work without pay during lunch breaks, as well as complaints that it generally underpays employees.

Hyp: Wal-Mart complains about negative publicity. (don't know)

Wal-Mart
NNP
complains
VBZ
negative
JJ
publicity
NN
Wal-Mart:NNP   0.50 15.00 12.00   6.89
has:VBZ 15.50 10.00 12.00 15.00
received:VBN 15.50   7.11 11.42 11.87
a:DT 20.50 20.00 20.00 20.00
lot:NN   7.64 14.50 10.59   6.64
negative:JJ 12.50 12.00   0.00   9.27
publicity:NN   7.39 13.96   9.27   0.00
recently:RB 15.50 16.65 12.00 14.03
including:VBG 15.50   8.10 11.31 15.00
allegations:NNS   8.63 12.39 10.04   4.45
that:IN 20.50 20.00 20.00 20.00
it:PRP 12.50 15.00 15.00 12.00
used:VBD 15.50   7.98 11.86 12.89
illegal:JJ 12.50   9.39   9.89   8.95
workers:NNS   5.47 13.51 12.00   6.35
made:VBD 15.50   9.59 10.39 10.58
employees:NNS   6.64 13.84 12.00   7.31
work:NN   6.25 14.15 12.00   6.19
pay:NN   7.54 12.66 12.00   7.39
lunch:NN   8.45 11.86 12.00   8.62
breaks:NNS   8.24 12.96 12.00   8.03
as:RB 15.50 20.00 12.00 15.00
well:RB 15.50 20.00 12.00 15.00
as:IN 20.50 20.00 20.00 20.00
complaints:NNS   8.42   2.11 11.00   7.19
that:IN 20.50 20.00 20.00 20.00
it:PRP 12.50 15.00 15.00 12.00
generally:RB 15.50 19.70   9.41 14.05
underpays:VBZ 15.50 10.00 12.00 15.00
employees:NNS   6.64 13.84 12.00   7.31
NO_WORD 10.00 10.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.89 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "underpays" of "complaints" dropped on aligned hyp word "complains"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Wal-Mart" <-nsubj-- "received" vs. hyp "Wal-Mart" <-nsubj-- "complains", which aligned to text "complaints" args have different parents, different relations: text "publicity" <-prep_of-- "lot" vs. hyp "publicity" <-prep_about-- "complains", which aligned to text "complaints"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.7148
Threshold: -1.8794


Inference ID: 238

Txt: Following the assassination attempt in 1981, Reagan said he felt God had spared him for a purpose, and he intended to devote the rest of his life in dedication to his God and to that purpose.

Hyp: Regan was almost assassinated in 1981. (yes)

Regan
NNP
was
VBD
almost
RB
assassinated
VBN
1981
CD
Following:VBG 15.50 10.00 20.00 10.00 20.50
the:DT 20.50 20.00 20.00 20.00 20.50
assassination:NN 10.50 15.00 15.00   1.00 19.26
attempt:NN 10.50 15.00 15.00 14.47 18.22
1981:CD 20.50 20.50 20.50 18.14   0.00
Reagan:NNP   3.64 15.50 15.50 15.50 20.50
said:VBD 15.50 10.00 20.00   9.34 19.94
he:PRP 12.50 15.00 20.00 15.00 20.50
felt:VBD 15.50 10.00 20.00   9.35 19.02
God:NNP 10.00 15.50 15.50 15.50 20.50
had:VBD 15.50 10.00 20.00 10.00 20.50
spared:VBN 15.50 10.00 20.00   8.34 19.82
he:PRP 12.50 15.00 20.00 15.00 20.50
a:DT 20.50 20.00 20.00 20.00 20.50
purpose:NN 10.50 15.00 15.00 15.00 19.24
he:PRP 12.50 15.00 20.00 15.00 20.50
intended:VBD 15.50 10.00 20.00 10.00 19.18
to:TO 20.50 20.00 20.00 20.00 20.50
devote:VB 15.50 10.00 20.00 10.00 19.97
the:DT 20.50 20.00 20.00 20.00 20.50
rest:NN 10.50 15.00 15.00 15.00 20.15
his:PRP$ 12.50 15.00 20.00 15.00 20.50
life:NN 10.50 15.00 15.00 14.71 19.42
dedication:NN 10.50 15.00 15.00 13.92 19.19
his:PRP$ 12.50 15.00 20.00 15.00 20.50
God:NNP 10.50 15.00 15.00 15.00 20.50
that:DT 20.50 20.00 20.00 20.00 20.50
purpose:NN 10.50 15.00 15.00 15.00 19.24
NO_WORD 10.00   1.00   9.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.83 Alignment.score
 1.00  0.86 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added almost[almost-RB]
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1981
 0.50  1.00 Factive.unknownPassage : implicative unknown : attempt-NN
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : almost
-2.00  1.00 Person.mismatch : person mimatch between Regan and Reagan
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "1981" <-prep_in-- "attempt" vs. hyp "1981" <-prep_in-- "assassinated", which aligned to text "assassination" args have different parents, different relations: text "Reagan" <-nsubj-- "said" vs. hyp "Regan" <-nsubjpass-- "assassinated", which aligned to text "assassination"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -7.3228
Threshold: -1.8794


Inference ID: 2189

Txt: Abu Eisa al-Hindi was said to have been involved in a plot to attack Heathrow airport, details of which were allegedly discovered on the computer of Abu Eisa al-Hindi, 25, an al-Qa'ida suspect recently arrested in Pakistan.

Hyp: Abu Eisa al-Hindi is the father of Abu Eisa al-Hindi. (don't know)

Abu_Eisa_al-Hindi
NNP
is
VBZ
the
DT
father
NN
Abu_Eisa_al-Hindi
NNP
Abu_Eisa_al-Hindi:NNP   0.00 15.50 20.50   9.27   0.00
was:VBD 15.50   0.00 20.00 15.00 15.50
said:VBN 15.50 10.00 20.00 15.00 15.50
to:TO 20.50 20.00 10.00 20.00 20.50
have:VB 15.50 10.00 20.00 15.00 15.50
been:VBN 15.50   0.00 20.00 15.00 15.50
involved:VBN 15.50 10.00 20.00 15.00 15.50
a:DT 20.50 20.00 10.00 20.00 20.50
plot:NN   9.43 15.00 20.00   7.83   9.43
to:TO 20.50 20.00 10.00 20.00 20.50
attack:VB 15.50 10.00 20.00 14.99 15.50
Heathrow:NNP 10.50 15.50 20.50 10.50 10.50
airport:NN   9.33 15.00 20.00   8.50   9.33
details:NNS   9.64 15.00 18.74   8.96   9.64
of:IN 20.50 20.00 18.70 20.00 20.50
which:WDT 20.50 20.00 10.00 20.00 20.50
were:VBD 15.50   0.00 20.00 15.00 15.50
allegedly:RB 15.50 20.00 20.00 13.31 15.50
discovered:VBN 15.50 10.00 20.00 11.40 15.50
the:DT 20.50 20.00   0.00 20.00 20.50
computer:NN   8.53 15.00 20.00   7.59   8.53
Abu_Eisa_al-Hindi:NNP   0.00 15.50 20.50   9.27   0.00
25:CD 20.50 20.50 20.50 20.50 20.50
an:DT 20.50 20.00 10.00 20.00 20.50
al-Qa:NN   0.50 15.00 20.00   8.77   0.50
ida:NN 10.21 15.00 20.00   7.66 10.21
suspect:VBP 15.50 10.00 20.00 14.01 15.50
recently:RB 15.50 20.00 20.00 12.64 15.50
arrested:VBN 15.50 10.00 20.00 13.67 15.50
Pakistan:NNP   9.78 15.50 20.50   9.61   9.78
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.18 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "25" of "Abu_Eisa_al-Hindi" dropped on aligned hyp word "Abu_Eisa_al-Hindi"
 1.00  1.00 Factive.factivePassage : factive entails : discovered-VBN
-0.10  1.00 NullPunisher.article : the
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "father" aligned badly to "computer"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Abu_Eisa_al-Hindi" <-xsubj-- "involved" vs. hyp "Abu_Eisa_al-Hindi" <-nsubj-- "father", which aligned to text "computer" args have different parents, different relations: text "Abu_Eisa_al-Hindi" <-nsubjpass-- "said" vs. hyp "Abu_Eisa_al-Hindi" <-nsubj-- "father", which aligned to text "computer"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.0278
Threshold: -1.8794


Inference ID: 182

Txt: AT&T Corp. says it will stop selling traditional local and long-distance residential service in seven states, blaming its move on a court decision that it says will result in higher prices for its use of regional networks.

Hyp: AT&T blames a court decision that will cause higher prices. (yes)

AT&T
NNP
blames
VBZ
a
DT
court
NN
decision
NN
that
WDT
will
MD
cause
VB
higher
JJR
prices
NNS
AT&T_Corp.:NNP   0.00 15.50 20.50   8.14   9.26 20.50 20.50 15.50 12.50   8.69
says:VBZ 15.50   5.95 20.00 15.00 15.00 20.00 20.00 10.00 12.00 15.00
it:PRP 12.50 15.00 20.00 12.00 12.00 17.78 20.00 15.00 15.00 12.00
will:MD 20.50 20.00 10.00 20.00 20.00 10.00   0.00 20.00 20.00 20.00
stop:VB 15.50   9.66 20.00 13.21 13.19 20.00 20.00   8.15 12.00 15.00
selling:VBG 15.50   9.16 20.00 15.00 15.00 20.00 20.00   9.33   9.52 12.51
traditional:JJ 12.50 10.07 20.00 12.00 12.00 20.00 20.00 11.42   9.20 11.86
local:JJ 12.50 10.63 20.00 11.57 12.00 20.00 20.00 12.00   9.35 12.00
long-distance:JJ 12.50 12.00 20.00 11.25 11.28 20.00 20.00 11.51   9.81 11.81
residential:JJ 12.50 11.83 20.00 12.00 12.00 20.00 20.00 11.76   8.52   9.20
service:NN   9.07 14.69 20.00   7.86   8.06 20.00 20.00 15.00 11.06   7.32
seven:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.18 20.50
states:NNS   7.93 15.00 20.00   6.21   7.93 20.00 20.00 15.00 12.00   7.16
blaming:VBG 15.50   0.00 20.00 15.00 14.86 20.00 20.00   8.66 11.21 14.55
its:PRP$ 12.50 15.00 20.00 12.00 12.00 20.00 20.00 15.00 15.00 12.00
move:NN   8.77 13.52 20.00   7.49   7.71 20.00 20.00 14.92 10.99   6.90
a:DT 20.50 20.00   0.00 20.00 20.00 10.00 10.00 20.00 20.00 20.00
court:NN   8.14 15.00 20.00   0.00   2.73 20.00 20.00 14.33 11.67   7.39
decision:NN   9.26 15.00 20.00   2.73   0.00 20.00 20.00 14.36 11.43   7.61
that:IN 20.50 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.00 20.00
it:PRP 12.50 15.00 20.00 12.00 12.00 17.78 20.00 15.00 15.00 12.00
says:VBZ 15.50   5.95 20.00 15.00 15.00 20.00 20.00 10.00 12.00 15.00
will:MD 20.50 20.00 10.00 20.00 20.00 10.00   0.00 20.00 20.00 20.00
result:VB 15.50   7.48 20.00 13.96 13.77 20.00 20.00   5.47   7.49 12.95
higher:JJR 12.50 11.51 20.00 11.67 11.43 20.00 20.00   8.66   0.00   5.83
prices:NNS   8.69 14.24 20.00   7.39   7.61 20.00 20.00 12.92   5.83   0.00
its:PRP$ 12.50 15.00 20.00 12.00 12.00 20.00 20.00 15.00 15.00 12.00
use:NN   8.86 14.35 20.00   7.60   7.81 20.00 20.00 14.02 10.77   7.02
regional:JJ 12.50 10.86 20.00 12.00 12.00 20.00 20.00 12.00 10.00 12.00
networks:NNS   8.45 15.00 20.00   7.09   8.39 20.00 20.00 15.00 12.00   7.72
NO_WORD 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.46 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added cause[cause-VB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "use" of "prices" dropped on aligned hyp word "prices"
-1.00  1.00 NullPunisher.other : cause
-1.00  1.00 NullPunisher.other : blames
-0.05  1.00 NullPunisher.aux : will
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "blames" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.5253
Threshold: -1.8794


Inference ID: 1003

Txt: Infertile women are vital for successful child-rearing despite being unable to produce children themselves.

Hyp: Infertile women cannot produce children. (yes)

Infertile
NNP
women
NNS
can
MD
not
RB
produce
VB
children
NNS
Infertile:JJ   0.50 12.00 20.00 12.00 12.00 12.00
women:NNS 10.04   0.00 20.00 15.00 14.83   5.32
are:VBP 15.50 15.00 20.00 20.00 10.00 15.00
vital:JJ 12.50 11.53 20.00 12.00 11.41 11.82
for:IN 20.50 20.00 20.00 20.00 20.00 20.00
successful:JJ 12.50 10.50 20.00 12.00 10.12 11.48
child-rearing:NN 10.23   6.76 20.00 15.00 15.00   0.00
despite:IN 20.50 19.77 20.00 20.00 18.32 19.51
being:VBG 15.50 15.00 20.00 20.00 10.00 15.00
unable:JJ 12.50 11.09 20.00 12.00   9.79   8.77
to:TO 20.50 18.72 10.00 20.00 20.00 20.00
produce:VB 15.50 14.83 18.79 20.00   0.00 14.71
children:NNS 10.23   5.32 20.00 15.00 14.71   0.00
children:NNS 10.23   5.32 20.00 15.00 14.71   0.00
NO_WORD 10.00 10.00 10.00   9.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.48 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-6.00  1.00 Adjunct.diffPol : hyp and txt have different polarity
-6.00  1.00 Modal.no : actual -> not possible
 0.00  1.00 NegPolarity.hypNegWord : "produce": has child with relation "neg"
 0.00  1.00 NegPolarity.hypNegRoot : "produce": has child with relation "neg"
-1.00  1.00 NullPunisher.other : not
-0.05  1.00 NullPunisher.aux : can
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "women" <-nsubj-- "vital" vs. hyp "women" <-nsubj-- "produce", which aligned to text "produce"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -16.6096
Threshold: -1.8794


Inference ID: 1002

Txt: The so-called "grandmother hypothesis", based on studies of African hunter-gatherer groups, suggests that infertile women are vital for successful child-rearing despite being unable to produce children themselves.

Hyp: Infertile women are vital for successful child-rearing, according to the "grandmother hypothesis". (yes)

Infertile
JJ
women
NNS
are
VBP
vital
JJ
successful
JJ
child-rearing
NN
the
DT
grandmother
NN
hypothesis
NN
The:DT 20.00 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00
so-called:JJ 10.00 12.00 12.00 10.00 10.00 12.00 20.00 12.00 12.00
grandmother:NN 12.00   8.16 15.00 12.00 12.00   4.49 20.00   0.00 10.00
hypothesis:NN 12.00   8.87 15.00 11.75 12.00   9.85 20.00 10.00   0.00
based:VBN 12.00 15.00 10.00 11.04 10.51 15.00 20.00 15.00 12.90
studies:NNS 12.00   6.69 15.00 10.32 11.06   8.43 20.00   9.29   4.55
African:NNP 12.50   8.04 15.50 12.50 12.50   8.60 20.50   9.00 10.50
hunter-gatherer:JJ 10.00 11.33 12.00 10.00   9.71 11.64 20.00 11.13 10.45
groups:NNS 12.00   5.31 15.00 10.52 10.95   6.15 20.00   7.72   9.02
suggests:VBZ 12.00 13.10 10.00   8.90 11.12 14.14 20.00 15.00 11.90
that:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
infertile:JJ   0.00   7.31 12.00   9.88 10.00 10.09 20.00 11.28   9.67
women:NNS 12.00   0.00 15.00 11.53 10.50   6.76 20.00   8.16   8.87
are:VBP 12.00 15.00   0.00 12.00 12.00 15.00 20.00 15.00 15.00
vital:JJ 10.00 11.53 12.00   0.00   6.83 11.63 20.00 12.00 11.75
for:IN 20.00 20.00 20.00 20.00 20.00 20.00 17.35 20.00 20.00
successful:JJ 10.00 10.50 12.00   6.83   0.00 11.84 20.00 12.00 12.00
child-rearing:NN 12.00   6.76 15.00 11.63 11.84   0.00 20.00   4.49   9.85
despite:IN 20.00 19.77 20.00 15.94 16.46 19.83 20.00 19.98 20.00
being:VBG 12.00 15.00   0.00 12.00 12.00 15.00 20.00 15.00 15.00
unable:JJ 10.00 11.09 12.00   9.33   8.61   8.88 20.00   8.40 12.00
to:TO 20.00 18.72 20.00 20.00 20.00 20.00 10.00 20.00 20.00
produce:VB 12.00 14.83 10.00 11.41 10.12 15.00 20.00 15.00 14.24
children:NNS 12.00   5.32 15.00 11.82 11.48   0.00 20.00   4.81 10.00
children:NNS 12.00   5.32 15.00 11.82 11.48   0.00 20.00   4.81 10.00
NO_WORD   9.00 10.00   1.00   9.00   9.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.97 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "studies" of "hypothesis" dropped on aligned hyp word "hypothesis"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "child-rearing" <-dep-- "successful" vs. hyp "child-rearing" <-prep_for-- "vital", which aligned to text "vital" args have different parents, different relations: text "hypothesis" <-nsubjpass-- "based" vs. hyp "hypothesis" <-prep_according_to-- "vital", which aligned to text "vital" args have different parents, different relations: text "hypothesis" <-nsubj-- "suggests" vs. hyp "hypothesis" <-prep_according_to-- "vital", which aligned to text "vital"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.8838
Threshold: -1.8794


Inference ID: 984

Txt: Cool, humid weather Sunday helped slow the advance of a fire that caused the evacuation of hundreds of homes and businesses in Alaska's Interior.

Hyp: There was a fire in Alaska. (yes)

There
EX
was
VBD
a
DT
fire
NN
Alaska
NNP
Cool:NNP 20.50 15.50 20.50 10.17   9.89
humid:JJ 20.00 12.00 20.00 10.39 12.50
weather:NN 20.00 15.00 20.00   7.67   9.15
Sunday:NNP 20.50 15.50 20.50   9.47   9.69
helped:VBD 20.00 10.00 20.00 15.00 15.50
slow:VB 20.00 10.00 20.00 15.00 15.50
the:DT 10.00 20.00 10.00 20.00 20.50
advance:NN 20.00 15.00 20.00   6.24   9.50
a:DT 10.00 20.00   0.00 20.00 20.50
fire:NN 20.00 15.00 20.00   0.00   9.80
that:WDT 10.00 20.00 10.00 20.00 20.50
caused:VBD 20.00 10.00 20.00 11.91 15.50
the:DT 10.00 20.00 10.00 20.00 20.50
evacuation:NN 20.00 15.00 20.00   4.73 10.04
hundreds:NNS 20.00 15.00 20.00   6.45   9.56
homes:NNS 20.00 15.00 20.00   8.07   7.78
businesses:NNS 20.00 15.00 20.00   7.66   8.53
Alaska:NNP 20.50 15.50 20.50   9.80   0.00
Interior:NNP 20.50 15.50 20.50   9.65   8.15
NO_WORD   1.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.00 Alignment.score
 1.00  0.88 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "caused" of "fire" dropped on aligned hyp word "fire"
-0.10  1.00 NullPunisher.functionWord : There
-0.05  1.00 NullPunisher.aux : was
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "was" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.8663
Threshold: -1.8794


Inference ID: 180

Txt: The Securities and Exchange Commission's new rule to beef up the independence of mutual fund boards represents an industry defeat.

Hyp: The SEC's new rule will give boards independence. (yes)

The
DT
SEC
NNP
new
JJ
rule
NN
will
MD
give
VB
boards
NNS
independence
NN
The:DT   0.00 20.50 20.00 20.00 10.00 20.00 20.00 20.00
Securities_and_Exchange_Commission:NNP 20.50   0.00 12.50   9.00 20.50 15.50   9.70   9.57
new:JJ 20.00 12.50   0.00 11.22 20.00 11.02 11.62 11.97
rule:NN 20.00   8.56 11.22   0.00 20.00 12.47   8.60   7.45
to:TO 10.00 20.50 20.00 20.00 10.00 20.00 20.00 20.00
beef_up:VB 20.00 15.50 12.00 15.00 20.00 10.00 15.00 15.00
the:DT   0.00 20.50 20.00 20.00 10.00 20.00 20.00 20.00
independence:NN 20.00   9.23 11.97   7.45 20.00 13.68   9.33   0.00
mutual_fund:NNS 20.00   9.84 12.00   9.31 20.00 15.00   9.72   9.65
boards:NNS 20.00   9.39 11.62   8.60 20.00 12.76   0.00   9.33
represents:VBZ 20.00 15.50 11.73 13.23 20.00   9.34 13.53 15.00
an:DT 10.00 20.50 20.00 20.00 10.00 20.00 20.00 20.00
industry:NN 20.00   7.77 12.00   7.21 20.00 15.00   8.23   8.05
defeat:NN 20.00   9.25 11.88   8.28 20.00 13.88   9.34   6.48
NO_WORD   1.00 10.00   9.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.53 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "mutual_fund" of "boards" dropped on aligned hyp word "boards"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-0.05  1.00 NullPunisher.aux : will
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "give" aligned badly to "beef_up"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "rule" <-nsubj-- "represents" vs. hyp "rule" <-nsubj-- "give", which aligned to text "beef_up" args have different parents, different relations: text "boards" <-prep_of-- "independence" vs. hyp "boards" <-iobj-- "give", which aligned to text "beef_up"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.2189
Threshold: -1.8794


Inference ID: 191

Txt: Oil prices edged up today as Norwegian employers said they would respond to a strike by locking out all workers next week, threatening to shut off almost all oil and gas output.

Hyp: Norwegian employers caused oil prices to rise. (yes)

Norwegian
JJ
employers
NNS
caused
VBD
oil
NN
prices
NNS
to
TO
rise
VB
Oil:NNP 12.00   8.01 15.00   0.00   7.39 20.00 15.00
prices:NNS 12.00   7.97 11.41   4.25   0.00 20.00 10.32
edged_up:VBD 12.00 15.00 10.00 15.00 15.00 20.00 10.00
today:NN 12.00   8.55 14.70   8.06   7.55 20.00 13.62
as:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00
Norwegian:NNP   0.50   8.99 15.50   9.58   9.56 20.50 15.50
employers:NNS 12.00   0.00 15.00   8.01   7.97 20.00 15.00
said:VBD 12.00 15.00 10.00 14.26 15.00 20.00   9.93
employers:NNS 12.00   0.00 15.00   8.01   7.97 20.00 15.00
would:MD 20.00 20.00 20.00 20.00 20.00 10.00 20.00
respond:VB 12.00 15.00   7.49 14.68 15.00 20.00   9.78
a:DT 20.00 20.00 20.00 20.00 20.00 10.00 20.00
strike:NN 12.00   5.03 14.45   8.45   8.00 20.00 15.00
by:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00
locking:VBG 12.00 15.00   7.33 15.00 12.24 20.00   7.91
all:DT 20.00 20.00 20.00 20.00 20.00 10.00 20.00
workers:NNS 12.00   1.63 15.00   6.13   6.08 20.00 15.00
next:JJ 10.00 12.00 12.00 12.00 12.00 20.00 12.00
week:NN 12.00   8.22 15.00   7.68   7.10 20.00 15.00
threatening:VBG 12.00 13.28   6.68 13.86 14.53 20.00   9.37
to:TO 20.00 20.00 20.00 20.00 20.00   0.00 20.00
shut_off:VB 12.00 15.00 10.00 15.00 15.00 20.00 10.00
almost:RB 12.00 15.00 20.00 15.00 15.00 20.00 20.00
all:DT 20.00 20.00 20.00 20.00 20.00 10.00 20.00
oil:NN 12.00   8.01 12.84   0.00   4.25 20.00 13.46
gas:NN 12.00   8.66 11.93   1.15   4.23 20.00 13.33
output:NN 12.00   7.94 14.22   5.64   5.86 20.00 11.46
NO_WORD   9.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.26 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-0.10  1.00 NullPunisher.functionWord : to
-1.00  1.00 NullPunisher.other : caused
-1.00  1.00 NullPunisher.other : rise
-2.00  1.00 RootEntailment.unalignedRoot : "caused" not aligned to anything
Hand-tuned score (dot product of above): -2.0078
Threshold: -1.8794


Inference ID: 926

Txt: Startling new research into mobile phones claims they may reduce a man's sperm count by up to 30%.

Hyp: Male fertility may be affected by use of a mobile phones. (yes)

Male
JJ
fertility
NN
may
MD
be
VB
affected
VBN
use
NN
a
DT
mobile_phones
NNS
Startling:VBG 12.00 15.00 20.00 10.00 10.00 15.00 20.00 15.00
new:JJ 10.00 12.00 20.00 12.00 11.93 11.55 20.00 12.00
research:NN 12.00   7.30 20.00 15.00 15.00   5.96 20.00   8.64
mobile_phones:NNS 12.00   9.67 20.00 15.00 15.00   8.32 20.00   0.00
claims:VBZ 12.00 13.18 20.00 10.00   6.63 15.00 20.00 15.00
they:PRP 15.00 12.00 20.00 15.00 15.00 12.00 20.00 12.00
may:MD 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00
reduce:VB 12.00 13.94 20.00 10.00   8.07 12.10 20.00 15.00
a:DT 20.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00
man:NN 12.00   8.90 20.00 15.00 15.00   6.63 20.00   7.19
sperm_count:NN 12.00 10.00 20.00 15.00 15.00 10.00 20.00 10.00
up_to:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
30:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
%:NN 12.50 10.14 20.50 15.50 15.11 10.50 20.50 10.50
NO_WORD   9.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.61 Alignment.score
 1.00  0.83 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  7.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.12 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added Male[Male-JJ]
 2.00  1.00 Modal.yes : actual -> possible
-1.00  1.00 NullPunisher.other : Male
-0.05  1.00 NullPunisher.aux : may
-1.00  1.00 NullPunisher.other : affected
-0.10  1.00 NullPunisher.article : a
-1.00  1.00 NullPunisher.other : use
-1.00  1.00 NullPunisher.other : fertility
-0.05  1.00 NullPunisher.aux : be
-2.00  1.00 RootEntailment.unalignedRoot : "affected" not aligned to anything
Hand-tuned score (dot product of above): -4.3465
Threshold: -1.8794


Inference ID: 346

Txt: Ahern, who was travelling to Tokyo for an EU-Japan summit yesterday, will consult with other EU leaders by telephone later this week in an effort to find an agreed candidate to presidency of the European commission.

Hyp: EU leaders found an agreement about a candidate to the post of European commission president. (don't know)

EU
NNP
leaders
NNS
found
VBD
an
DT
agreement
NN
a
DT
candidate
NN
the
DT
post
NN
European
NNP
commission
NN
president
NN
Ahern:NNP 10.50 10.50 15.50 20.50 10.50 20.50 10.50 20.50 10.50 10.50 10.50 10.50
who:WP 12.50 12.00 15.00 20.00 12.00 20.00 12.00 20.00 12.00 12.50 12.00 12.00
was:VBD 15.50 15.00 10.00 20.00 15.00 20.00 15.00 20.00 15.00 15.50 15.00 15.00
traveling:VBG 15.50 13.55   7.15 20.00 13.55 20.00 13.93 20.00 13.62 15.50 15.00 13.29
Tokyo:NNP 10.00   9.03 15.50 20.50   8.07 20.50   8.92 20.50   7.62   7.97   8.43   7.99
an:DT 20.50 18.79 20.00   0.00 20.00   8.73 20.00 10.00 20.00 20.50 20.00 20.00
EU-Japan:JJ 12.50 12.00 12.00 20.00 12.00 20.00 12.00 20.00 12.00 12.50 12.00 12.00
summit:NN   9.66   6.28 15.00 20.00   4.80 20.00   8.87 20.00   8.72   9.00   8.05   5.93
yesterday:NN 10.50   8.08 14.46 20.00   6.05 20.00   8.38 20.00   8.20   8.43   7.38   7.43
will:MD 20.50 20.00 20.00 10.00 20.00 10.00 20.00 10.00 20.00 20.50 20.00 20.00
consult:VB 15.50 13.16   9.79 20.00 12.58 20.00 15.00 20.00 13.31 15.50 14.78 13.25
other:JJ 12.50 12.00 12.00 20.00 12.00 20.00 12.00 20.00 12.00 12.50 12.00 12.00
EU:NNP   0.00 10.50 15.50 20.50 10.50 20.50 10.50 20.50 10.50   9.00   9.45 10.50
leaders:NNS 10.50   0.00 15.00 18.79   6.05 20.00   8.72 20.00   8.57   8.83   6.38   7.90
telephone:NN 10.50   8.34 14.48 20.00   7.32 20.00   8.09 20.00   8.02   8.10   7.69   7.06
later:RBR 15.50 15.00 18.95 20.00 14.25 20.00 15.00 20.00 13.33 15.50 14.28 14.02
this:DT 20.50 20.00 20.00 10.00 20.00 10.00 20.00 10.00 20.00 20.50 20.00 20.00
week:NN 10.50   7.98 14.35 20.00   5.89 20.00   8.28 20.00   8.10   8.32   7.26   7.31
an:DT 20.50 18.79 20.00   0.00 20.00   8.73 20.00 10.00 20.00 20.50 20.00 20.00
effort:NN 10.50   6.86 15.00 20.00   6.00 20.00   7.69 20.00   7.47   7.64   6.48   6.54
to:TO 20.50 20.00 20.00   8.20 20.00 10.00 20.00 10.00 20.00 20.50 20.00 20.00
find:VB 15.50 15.00   0.00 20.00 15.00 20.00 15.00 20.00 14.62 15.50 15.00 15.00
an:DT 20.50 18.79 20.00   0.00 20.00   8.73 20.00 10.00 20.00 20.50 20.00 20.00
agreed:JJ 12.50   9.73 10.90 20.00   5.71 20.00 12.00 20.00 12.00 12.50 12.00 11.66
candidate:NN 10.50   8.72 14.82 20.00   7.84 20.00   0.00 20.00   8.45   8.12   8.17   4.99
presidency:NN   9.87   7.28 15.00 20.00   6.48 20.00   3.58 20.00   8.00   8.72   7.72   1.00
the:DT 20.50 20.00 20.00 10.00 20.00 10.00 20.00   0.00 20.00 20.50 20.00 20.00
European:JJ 12.50 12.00 12.00 20.00 12.00 20.00 12.00 20.00 12.00   0.50 12.00 12.00
commission:NN   9.45   6.38 15.00 20.00   6.67 20.00   8.17 20.00   7.97   8.19   0.00   7.16
NO_WORD 10.00 10.00 10.00   1.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.34 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  4.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added post[post-NN]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "other" of "leaders" dropped on aligned hyp word "leaders"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : post
-0.10  1.00 NullPunisher.article : a
-0.10  1.00 NullPunisher.article : the
 1.00  1.00 Quant.contract : [an,a]
-1.00  1.00 Structure.relMismatch : text "candidate" is dobj of "find" while hyp "candidate" is prep_about of "found" which aligned to text "find"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.8794
Threshold: -1.8794


Inference ID: 921

Txt: Saginaw police responded to a domestic violence complaint at the wrong house and ended up shooting the owners' dog.

Hyp: Saginaw police mistakenly went to the wrong house in response to a complaint. (yes)

Saginaw
NNP
police
NNS
mistakenly
RB
went
VBD
the
DT
wrong
JJ
house
NN
response
NN
a
DT
complaint
NN
Saginaw:NNP   0.00 10.50 15.50 15.50 20.50 12.50 10.50 10.50 20.50 10.50
police:NNS 10.50   0.00 11.51 15.00 20.00 12.00   7.66   8.35 20.00   8.53
responded:VBD 15.50 12.42 16.19   7.56 20.00   9.11 14.57   2.50 20.00 13.58
a:DT 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00   0.00 20.00
domestic_violence:NN 10.50 10.00 15.00 15.00 20.00 12.00 10.00 10.00 20.00 10.00
complaint:NN 10.50   8.53 13.06 12.92 20.00   9.54   8.20   7.79 20.00   0.00
the:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00
wrong:JJ 12.50 12.00 10.46   9.06 20.00   0.00 12.00   9.45 20.00   9.54
house:NN 10.50   7.66 13.70 14.94 20.00 12.00   0.00   8.00 20.00   8.20
ended_up:VBD 15.50 15.00 20.00 10.00 20.00 12.00 15.00 15.00 20.00 15.00
shooting:VBG 15.50   9.45 16.16   8.20 20.00 12.00 14.70 15.00 20.00 14.94
the:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00 20.00 10.00 20.00
owners:NNS 10.50   8.06 15.00 15.00 20.00 11.84   6.98   8.36 20.00   8.54
dog:NN 10.50   7.94 14.27 14.24 20.00   9.63   7.67   8.82 20.00   8.97
NO_WORD 10.00 10.00   9.00 10.00   1.00   9.00 10.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.42 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added mistakenly[mistakenly-RB]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "domestic_violence" of "complaint" dropped on aligned hyp word "complaint"
-1.00  1.00 NullPunisher.other : went
-1.00  1.00 NullPunisher.other : mistakenly
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "went" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.1797
Threshold: -1.8794


Inference ID: 980

Txt: President Bush returned to the Mountain State to celebrate Independence Day, telling a spirited crowd yesterday that on its 228th birthday the nation is "moving forward with confidence and strength."

Hyp: Independence Day was a popular movie. (don't know)

Independence_Day
NNP
was
VBD
a
DT
popular
JJ
movie
NN
President_Bush:NNP 10.00 15.00 20.00 12.00   8.08
returned:VBD 15.00 10.00 20.00 10.85 15.00
the:DT 20.00 20.00 10.00 20.00 20.00
Mountain_State:NNP 10.00 15.00 20.00 12.00 10.00
to:TO 20.00 20.00 10.00 20.00 20.00
celebrate:VB 15.00 10.00 20.00   9.64 14.36
Independence_Day:NNP   0.00 15.00 20.00 12.00 10.00
telling:VBG 15.00 10.00 20.00 12.00 14.54
a:DT 20.00 20.00   0.00 20.00 20.00
spirited:JJ 12.00 12.00 20.00   8.22   9.41
crowd:NN 10.00 15.00 20.00 10.30   8.22
yesterday:NN 10.00 15.00 20.00 12.00   7.08
that:IN 20.00 20.00 20.00 20.00 20.00
its:PRP$ 12.00 15.00 20.00 15.00 12.00
228th:JJ 12.00 12.00 20.00 10.00 12.00
birthday:NN 10.00 15.00 20.00 10.03   8.70
the:DT 20.00 20.00 10.00 20.00 20.00
nation:NN 10.00 15.00 20.00 10.02   7.47
is:VBZ 15.00   0.00 20.00 12.00 15.00
moving:VBG 15.00 10.00 20.00 12.00 10.00
forward:RB 15.00 20.00 20.00 12.00 15.00
confidence:NN 10.00 15.00 20.00 12.00   8.33
strength:NN 10.00 15.00 20.00 11.46   7.21
NO_WORD 10.00   1.00   1.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.90 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.20 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added popular[popular-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "confidence" of "moving" dropped on aligned hyp word "movie"
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: telling-VBG
-0.10  1.00 NullPunisher.article : a
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : popular
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "movie" aligned badly to "moving"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Independence_Day" <-dobj-- "celebrate" vs. hyp "Independence_Day" <-nsubj-- "movie", which aligned to text "moving"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -6.0632
Threshold: -1.8794


Inference ID: 917

Txt: Medicare will select by lottery 10 percent of those who apply by Aug. 16 to begin receiving benefits starting Sept. 1.

Hyp: Medicare will choose 10 percent of applicants to receive benefits by Aug. 16. (don't know)

Medicare
NNP
will
MD
choose
VB
10
CD
percent
NN
applicants
NNS
to
TO
receive
VB
benefits
NNS
Aug.
NNP
16
CD
Medicare:NNP   0.00 20.50 15.50 20.50   9.78   9.76 20.50 15.50   9.77   9.65 20.50
will:MD 20.50   0.00 20.00 20.50 20.50 20.00 10.00 20.00 20.00 20.50 20.50
select:VB 15.50 20.00   4.55 20.36 15.50 12.87 20.00   8.48 15.00 15.50 20.50
lottery:NN 10.26 20.00 14.06 19.84   9.88   9.36 20.00 13.62   9.21   9.76 20.18
10:CD 20.50 20.50 20.50   0.00 20.00 20.50 20.50 19.03 19.74 20.50   2.89
percent:NN   9.78 20.50 14.78 20.00   0.00   9.09 20.50 15.50   9.10   8.29 20.50
those:DT 20.50 10.00 20.00 20.50 20.50 20.00 10.00 20.00 20.00 20.50 20.50
who:WP 12.50 20.00 15.00 20.50 12.50 12.00 20.00 15.00 12.00 12.50 20.50
apply:VBP 15.50 20.00   5.99 20.10 14.56   2.50 20.00   8.22 13.41 15.50 20.25
by:RP 20.50 10.00 20.00 20.50 20.50 18.05   7.84 20.00 17.89 20.50 20.50
Aug.:NNP   9.65 20.50 15.50 20.50   8.29   8.89 20.50 15.50   8.90   0.00 20.00
16:CD 20.50 20.50 20.15   2.89 20.50 20.50 20.50 19.65 20.18 20.00   0.00
to:TO 20.50 10.00 20.00 20.50 20.50 20.00   0.00 20.00 20.00 20.50 20.50
begin:VB 15.50 20.00   6.98 20.37 14.75 14.95 20.00   7.88 14.55 15.50 20.50
receiving:VBG 15.50 20.00   7.56 19.15 15.50 12.53 20.00   0.00 11.78 15.50 18.80
benefits:NNS   9.77 20.00 12.24 19.74   9.10   6.95 20.00 13.12   0.00   8.90 20.18
starting:VBG 15.50 20.00   9.12 19.52 14.38 13.65 20.00 10.00 13.46 15.50 20.50
Sept.:NNP   9.56 20.50 15.50 20.50   8.13   8.76 20.50 15.50   8.77   2.98 20.00
1:CD 20.50 20.50 20.50   9.77 20.27 20.50 20.50 19.75 20.20 20.00   5.00
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.62 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.27 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "starting" of "benefits" dropped on aligned hyp word "benefits"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 08/16/1000
-0.05  1.00 NullPunisher.aux : will
-0.10  1.00 NullPunisher.functionWord : to
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "choose" aligned badly to "select"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "receiving" <-xcomp-- "begin" vs. hyp "receive" <-xcomp-- "choose", which aligned to text "select" args have different parents, different relations: text "receiving" <-partmod-- "begin" vs. hyp "receive" <-xcomp-- "choose", which aligned to text "select"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.8703
Threshold: -1.8794


Inference ID: 172

Txt: The terrorist is suspected of being behind several deadly kidnappings and dozens of suicide attacks in Iraq.

Hyp: The terrorist may have caused suicide attacks and kidnappings. (yes)

The
DT
terrorist
JJ
may
MD
have
VB
caused
VBN
suicide
NN
attacks
NNS
kidnappings
NNS
The:DT   0.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00
terrorist:JJ 20.00   0.00 20.00 12.00 10.54   8.94   6.11   7.28
is:VBZ 20.00 12.00 20.00 10.00 10.00 15.00 15.00 15.00
suspected:VBN 20.00   6.00 20.00 10.00   5.66   9.96   9.66   9.44
of:IN 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00
being:VBG 20.00 12.00 20.00 10.00 10.00 15.00 15.00 15.00
several:JJ 20.00 10.00 20.00 12.00 12.00 12.00 12.00 12.00
deadly:JJ 20.00   4.22 20.00 12.00   6.91   7.99   6.01   8.41
kidnappings:NNS 20.00   7.28 20.00 15.00 13.15   5.58   6.34   0.00
dozens:NNS 20.00   8.93 20.00 15.00 13.70   8.55   7.33   7.00
suicide:NN 20.00   8.94 20.00 15.00 11.79   0.00   7.18   5.58
attacks:NNS 20.00   6.11 20.00 15.00 10.98   7.18   0.00   6.34
Iraq:NNP 20.50 12.50 20.50 15.50 15.50 10.38   9.64 10.34
NO_WORD   1.00   9.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.46 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "deadly" of "kidnappings" dropped on aligned hyp word "kidnappings"
 2.00  1.00 Modal.yes : actual -> possible
-0.05  1.00 NullPunisher.aux : have
-1.00  1.00 NullPunisher.other : caused
-0.05  1.00 NullPunisher.aux : may
-2.00  1.00 RootEntailment.unalignedRoot : "caused" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.3086
Threshold: -1.8794


Inference ID: 918

Txt: Toshiba has produced a fuel cell with no moving parts.

Hyp: Toshiba has no moving parts. (don't know)

Toshiba
NNP
has
VBZ
no
RB
moving
JJ
parts
NNS
Toshiba:NNP   0.00 15.50 15.50 12.50 10.50
has:VBZ 15.50   0.00 20.00 12.00 15.00
produced:VBN 15.50 10.00 20.00 12.00 13.74
a:DT 20.50 20.00 20.00 20.00 20.00
fuel_cell:NN 10.50 15.00 15.00 12.00 10.00
no:DT 20.50 20.00   0.00 20.00 20.00
moving:VBG 15.50 10.00 20.00   0.00 14.65
parts:NNS 10.50 15.00 15.00 11.65   0.00
NO_WORD 10.00 10.00   9.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.61 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.00  1.00 NegPolarity.hypNegWord : "no": is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord : "no": is in NegPolarityMarkers list
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Toshiba" <-nsubj-- "produced" vs. hyp "Toshiba" <-nsubj-- "has", which aligned to text "has" args have different parents, different relations: text "parts" <-prep_with-- "produced" vs. hyp "parts" <-dobj-- "has", which aligned to text "has"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.00  1.00 NegPolarity.txtNegWord&NegPolarity.hypNegWord :
Hand-tuned score (dot product of above): -2.8968
Threshold: -1.8794


Inference ID: 2180

Txt: Speaking outside the hospital moments after getting her child back, Ciancio said she was confident all along that the baby would be found.

Hyp: Ciancio was hospitalized for speaking to the baby. (don't know)

Ciancio
NNP
was
VBD
hospitalized
VBN
for
IN
speaking
VBG
the
DT
baby
NN
Speaking:VBG 15.50 10.00 10.00 20.00   0.00 20.00 15.00
the:DT 20.50 20.00 20.00 17.35 20.00   0.00 20.00
hospital:NN 10.50 15.00   6.00 20.00 14.15 20.00   5.88
moments:NNS 10.50 15.00 15.00 20.00 14.16 20.00   8.08
after:IN 20.50 20.00 20.00   6.82 20.00 17.88 20.00
getting:VBG 15.50 10.00   9.34 20.00   9.24 20.00 14.89
her:PRP$ 12.50 15.00 15.00 20.00 15.00 20.00 12.00
child:NN 10.50 15.00 11.19 20.00 15.00 20.00   4.18
back:RB 15.50 20.00 19.24 20.00 20.00 20.00 13.09
Ciancio:NNP   0.00 15.50 15.50 20.50 15.50 20.50 10.50
said:VBD 15.50 10.00   9.41 20.00   7.16 20.00 15.00
her:PRP$ 12.50 15.00 15.00 20.00 15.00 20.00 12.00
was:VBD 15.50   0.00 10.00 20.00 10.00 20.00 15.00
confident:JJ 12.50 12.00 11.65 20.00 10.31 20.00 12.00
all:DT 20.50 20.00 20.00 20.00 20.00 10.00 20.00
along:IN 20.50 20.00 20.00 10.00 20.00 20.00 20.00
that:IN 20.50 20.00 20.00 10.00 20.00 20.00 20.00
the:DT 20.50 20.00 20.00 17.35 20.00   0.00 20.00
baby:NN 10.50 15.00 11.81 20.00 15.00 20.00   0.00
would:MD 20.50 20.00 20.00 20.00 20.00 10.00 20.00
be:VB 15.50   0.00 10.00 20.00 10.00 20.00 15.00
found:VBN 15.50 10.00   5.88 20.00   8.70 20.00 14.11
NO_WORD 10.00   1.00 10.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.46 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "moments" of "Speaking" dropped on aligned hyp word "speaking"
-1.00  1.00 NullPunisher.other : for
-0.05  1.00 NullPunisher.aux : was
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "hospitalized" aligned badly to "hospital"
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Ciancio" <-nsubj-- "said" vs. hyp "Ciancio" <-nsubjpass-- "hospitalized", which aligned to text "hospital"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -4.5405
Threshold: -1.8794


Inference ID: 179

Txt: The SEC's new rule to beef up the independence of mutual fund boards represents an industry defeat.

Hyp: Industries were defeated by the SEC. (don't know)

Industries
NNP
were
VBD
defeated
VBN
the
DT
SEC
NNP
The:DT 20.00 20.00 20.00   0.00 20.50
SEC:NNP   7.77 15.50 15.50 20.50   0.00
new:JJ 12.00 12.00 11.53 20.00 12.50
rule:NN   7.21 15.00 14.06 20.00   8.56
to:TO 20.00 20.00 20.00 10.00 20.50
beef_up:VB 15.00 10.00 10.00 20.00 15.50
the:DT 20.00 20.00 20.00   0.00 20.50
independence:NN   8.05 15.00 12.90 20.00   9.23
mutual_fund:NNS   7.42 15.00 15.00 20.00   9.84
boards:NNS   8.27 15.00 15.00 20.00   9.39
represents:VBZ 15.00 10.00   9.22 20.00 15.50
an:DT 20.00 20.00 20.00 10.00 20.50
industry:NN   0.00 15.00 15.00 20.00   7.77
defeat:NN   8.09 15.00   0.00 20.00   9.25
NO_WORD 10.00   1.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.37 Alignment.score
 1.00  0.91 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : were
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "industry" <-dobj-- "represents vs. hyp "Industries" <-nsubjpass-- "defeated", which aligned to text "defeat" args have different parents, different relations: text "SEC" <-poss-- "rule" vs. hyp "SEC" <-agent-- "defeated", which aligned to text "defeat"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -3.5280
Threshold: -1.8794


Inference ID: 973

Txt: Political leaders pledged that construction of the so-called Freedom Tower -- which will rise 1,776 feet into the air and be the world's tallest building -- will be finished on schedule by the end of 2008.

Hyp: The Freedom Tower will be the world's tallest building. (yes)

The
DT
Freedom_Tower
NNP
will
MD
be
VB
the
DT
world
NN
tallest
JJS
building
NN
Political_leaders:NNS 20.00   8.29 20.00 15.00 20.00   7.44 11.08   5.90
pledged:VBD 20.00 15.50 20.00 10.00 20.00 14.03 12.00 15.00
that:IN 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00
construction:NN 20.00   8.72 20.00 15.00 20.00   7.91   9.58   3.94
the:DT   0.00 20.50 10.00 20.00   0.00 20.00 20.00 20.00
so-called:JJ 20.00 12.50 20.00 12.00 20.00 12.00 10.00 12.00
Freedom_Tower:NNP 20.50   0.00 20.50 15.50 20.50   8.64 12.50   8.06
which:WDT 10.00 20.50 10.00 20.00 10.00 20.00 20.00 20.00
will:MD 10.00 20.50   0.00 20.00 10.00 20.00 20.00 20.00
rise:VB 20.00 15.50 20.00 10.00 20.00 15.00 12.00 15.00
1,776:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50
feet:NNS 20.00   9.14 20.00 15.00 20.00   8.37   8.59   6.05
the:DT   0.00 20.50 10.00 20.00   0.00 20.00 20.00 20.00
air:NN 20.00   8.88 20.00 15.00 20.00   8.09 12.00   6.95
be:VB 20.00 15.50 20.00   0.00 20.00 15.00 12.00 15.00
the:DT   0.00 20.50 10.00 20.00   0.00 20.00 20.00 20.00
world:NN 20.00   8.64 20.00 15.00 20.00   0.00   8.22   7.18
tallest:JJS 20.00 12.50 20.00 12.00 20.00   8.22   0.00   6.69
building:NN 20.00   8.06 20.00 15.00 20.00   7.18   6.69   0.00
will:MD 10.00 20.50   0.00 20.00 10.00 20.00 20.00 20.00
be:VB 20.00 15.50 20.00   0.00 20.00 15.00 12.00 15.00
finished:VBN 20.00 15.50 20.00 10.00 20.00 12.21 10.48 15.00
schedule:NN 20.00   9.17 20.00 15.00 20.00   8.41 12.00   7.87
the:DT   0.00 20.50 10.00 20.00   0.00 20.00 20.00 20.00
end:NN 20.00   8.42 20.00 15.00 20.00   7.57 11.83   6.27
2008:CD 20.50 20.50 20.50 20.50 20.50 19.88 19.82 20.39
NO_WORD   1.00 10.00 10.00 10.00   1.00 10.00   9.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.91 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.62 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "so-called" of "Freedom_Tower" dropped on aligned hyp word "Freedom_Tower"
 0.00  1.00 NegPolarity.hypNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord : "tallest": tag "JJS" is in NegPolarityMarkers list
-0.05  1.00 NullPunisher.aux : will
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Freedom_Tower" <-prep_of-- "construction" vs. hyp "Freedom_Tower" <-nsubj-- "be", which aligned to text "be"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
 0.00  1.00 NegPolarity.txtNegWord&NegPolarity.hypNegWord :
Hand-tuned score (dot product of above): -0.7276
Threshold: -1.8794


Inference ID: 992

Txt: Wal-Mart Stores has asked a US federal appeals court to review a judge's order approving class-action status for a sex-discrimination lawsuit.

Hyp: The judge approves of sex-discrimination. (don't know)

The
DT
judge
NN
approves
VBZ
sex-discrimination
NN
Wal-Mart_Stores:NNP 20.50   7.06 15.50   7.32
has:VBZ 20.00 15.00 10.00 15.00
asked:VBN 20.00 13.36   8.95 13.76
a:DT 10.00 20.00 20.00 20.00
US:NNP 20.50   7.28 15.50   7.36
federal:JJ 20.00   7.96   8.15 10.20
appeals:NNS 20.00   1.28 12.79   6.17
court:NN 20.00   0.61 12.90   5.91
to:TO 10.00 20.00 20.00 20.00
review:VB 20.00 10.95   6.15 12.68
a:DT 10.00 20.00 20.00 20.00
judge:NN 20.00   0.00 13.33   6.20
order:NN 20.00   3.63 12.69   7.56
approving:VBG 20.00 13.67   0.00 14.67
class-action:JJ 20.00 10.09 10.46   8.62
status:NN 20.00   6.95 11.70   6.42
a:DT 10.00 20.00 20.00 20.00
sex-discrimination:JJ 20.00   6.76 12.00   0.00
lawsuit:NN 20.00   2.20 13.66   6.36
NO_WORD   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.06 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "lawsuit" of "approving" dropped on aligned hyp word "approves"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-0.10  1.00 NullPunisher.article : The
 1.00  1.00 Quant.contract : [a,the]
-1.00  1.00 Structure.relMismatch : text "sex-discrimination" is prep_for of "approving" while hyp "sex-discrimination" is prep_of of "approves" which aligned to text "approving"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 4.0659
Threshold: -1.8794


Inference ID: 349

Txt: Protest votes as citizens of the 25 EU nations punished their governments for everything from high unemployment to involvement in Iraq.

Hyp: Citizens of the 25 EU nations are punished by their governments for everything. (don't know)

Citizens
NNPS
the
DT
25
CD
EU
NNP
nations
NNS
are
VBP
punished
VBN
their
PRP$
governments
NNS
everything
NN
Protest:JJ 12.00 20.00 20.50 12.50 12.00 12.00 12.00 15.00 12.00 12.00
votes:NNS   8.54 20.00 19.48 10.50   7.68 15.00 15.00 12.00   7.36 10.00
citizens:NNS   0.00 20.00 20.43 10.50   7.58 15.00 12.70 12.00   5.29 10.00
the:DT 20.00   0.00 20.50 20.50 20.00 20.00 20.00 20.00 20.00 20.00
25:CD 20.50 20.50   0.00 20.50 20.50 20.50 20.11 20.50 20.50 20.50
EU:NNP 10.50 20.50 20.50   0.00 10.50 15.50 15.50 12.50 10.50 10.50
nations:NNS   7.88 20.00 20.50 10.50   0.00 15.00 15.00 12.00   4.07 10.00
punished:VBN 15.00 20.00 20.11 15.50 15.00 10.00   0.00 15.00 13.18 15.00
their:PRP$ 12.00 20.00 20.50 12.50 12.00 15.00 15.00   0.00 12.00 10.79
governments:NNS   7.58 20.00 20.50 10.50   4.07 15.00 13.18 12.00   0.00 10.00
everything:NN 10.00 20.00 20.50 10.50 10.00 15.00 15.00 10.79 10.00   0.00
high:JJ 12.00 20.00 20.00 12.50 12.00 12.00 10.24 15.00 12.00 12.00
unemployment:NN   9.03 20.00 19.90 10.50   8.33 15.00 15.00 12.00   8.08 10.00
involvement:NN   8.66 20.00 20.50 10.50   7.84 15.00 12.36 12.00   7.54 10.00
Iraq:NNP   9.68 20.50 20.50   9.22   9.39 15.50 15.50 12.50   9.19 10.50
NO_WORD 10.00   1.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.93 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.90 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "unemployment" of "everything" dropped on aligned hyp word "everything"
-0.05  1.00 NullPunisher.aux : are
-3.00  1.00 Structure.clearBadness : for predicate punished, text undergoer set [vote] overlaps hyp actor set [Citizens]
-3.00  1.00 Structure.argsMismatch : passive struct without correct agent (votes vs. governments)
-1.00  1.00 Structure.relMismatch : text "governments" is dobj of "punished" while hyp "governments" is agent of "punished" which aligned to text "punished"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -5.6821
Threshold: -1.8794


Inference ID: 2185

Txt: The nine men, aged between 19 and 32, are being held at Paddington Green high security police station in central London.

Hyp: between 19 and 32 men are being held at Paddington Green high security police station in central London. (don't know)

between
IN
19
CD
and
CC
32
CD
men
NNS
are
VBP
being
VBG
held
VBN
Paddington_Green
NNP
high
JJ
security
NN
police_station
NN
central
JJ
London
NNP
The:DT 20.00 20.50 10.00 20.50 20.00 20.00 20.00 20.00 20.50 20.00 20.00 20.00 20.00 20.50
nine:CD 20.50   5.00 20.50   5.00 19.75 20.50 20.50 19.68 20.50 20.50 20.50 20.50 20.50 20.50
men:NNS 20.00 20.18 20.00 19.66   0.00 15.00 15.00 12.93   8.65 12.00   6.65 10.00 11.91   7.91
aged:VBN 20.00 20.34 20.00 20.05   8.90 10.00 10.00   8.37 15.50 12.00 14.77 15.00 12.00 15.50
19:CD 20.50   0.00 20.50   4.41 20.18 20.50 20.50 18.83 20.50 19.30 20.50 20.50 20.05 20.50
32:CD 20.50   4.41 20.50   0.00 19.66 20.50 20.50 19.84 20.50 19.09 20.30 20.50 20.33 20.50
are:VBP 20.00 20.50 20.00 20.50 15.00   0.00   0.00 10.00 15.50 12.00 15.00 15.00 12.00 15.50
being:VBG 20.00 20.50 20.00 20.50 15.00   0.00   0.00 10.00 15.50 12.00 15.00 15.00 12.00 15.50
held:VBN 20.00 18.83 20.00 19.84 12.93 10.00 10.00   0.00 15.50 11.13 14.50 15.00 11.98 15.50
Paddington_Green:NNP 20.50 20.50 20.50 20.50   8.65 15.50 15.50 15.50   0.00 12.50   8.95 10.50 12.50   8.92
high:JJ 20.00 19.30 20.00 19.09 12.00 12.00 12.00 11.13 12.50   0.00 12.00 12.00   9.25 12.50
security:NN 20.00 20.50 20.00 20.30   6.65 15.00 15.00 14.50   8.95 12.00   0.00 10.00 12.00   8.28
police_station:NN 20.00 20.50 20.00 20.50 10.00 15.00 15.00 15.00 10.50 12.00 10.00   0.00 12.00 10.50
central:JJ 20.00 20.05 20.00 20.33 11.91 12.00 12.00 11.98 12.50   9.25 12.00 12.00   0.00 12.50
London:NNP 20.50 20.50 20.50 20.50   7.91 15.50 15.50 15.50   8.92 12.50   8.28 10.50 12.50   0.00
NO_WORD 10.00 10.00 10.00 10.00 10.00   1.00   1.00 10.00 10.00   9.00 10.00 10.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.76 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  11.00 Alignment.hypSpan
 0.10  0.79 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "aged" of "men" dropped on aligned hyp word "men"
-1.00  1.00 NullPunisher.other : and
-1.00  1.00 NullPunisher.other : between
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.6822
Threshold: -1.8794


Inference ID: 923

Txt: The Arabic-language television network Al-Jazeera reports it has received a statement and a videotape from militants.

Hyp: Al-Jazeera is an Arabic-language television network. (yes)

Al-Jazeera
NNP
is
VBZ
an
DT
Arabic-language
JJ
television
NN
network
NN
The:DT 20.50 20.00 10.00 20.00 20.00 20.00
Arabic-language_television_network_Al-Jazeera:NNP   5.00 15.50 20.50   7.50   5.50 10.00
reports:VBZ 15.50 10.00 20.00 12.00 13.80 14.44
it:PRP 12.50 15.00 20.00 15.00 12.00 12.00
has:VBZ 15.50   8.64 20.00 12.00 15.00 15.00
received:VBN 15.50 10.00 20.00 12.00 13.22 13.65
a:DT 20.50 20.00   8.73 20.00 20.00 20.00
statement:NN   8.19 15.00 20.00 11.16   6.64   7.03
a:DT 20.50 20.00   8.73 20.00 20.00 20.00
videotape:NN   9.84 15.00 20.00 11.09   4.93   6.79
militants:NNS   9.50 15.00 20.00 11.49   8.23   8.94
NO_WORD 10.00   1.00   1.00   9.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.45 Alignment.score
 1.00  0.81 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  1.00 Alignment.hypSpan
 0.10  0.17 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added television[television-NN]
-1.00  1.00 NullPunisher.other : Arabic-language
-0.05  1.00 NullPunisher.aux : is
-1.00  1.00 NullPunisher.other : television
-0.10  1.00 NullPunisher.article : an
-1.00  1.00 NullPunisher.other : network
-2.00  1.00 RootEntailment.unalignedRoot : "network" not aligned to anything
Hand-tuned score (dot product of above): -5.2719
Threshold: -1.8794


Inference ID: 991

Txt: Wal-Mart Stores has asked a US federal appeals court to review a judge's order approving class-action status for a sex-discrimination lawsuit.

Hyp: A lawsuit was filed against Wal-Mart. (yes)

A
DT
lawsuit
NN
was
VBD
filed
VBN
Wal-Mart
NNP
Wal-Mart_Stores:NNP 20.50   7.55 15.50 15.50   0.00
has:VBZ 20.00 15.00 10.00 10.00 15.50
asked:VBN 20.00 14.01 10.00   8.22 15.50
a:DT   0.00 20.00 20.00 20.00 20.50
US:NNP 20.50   7.59 15.50 15.50   6.38
federal:JJ 20.00   7.69 12.00   8.45 12.50
appeals:NNS 20.00   2.50 15.00   7.87   7.88
court:NN 20.00   1.99 15.00   7.29   7.77
to:TO 10.00 20.00 20.00 20.00 20.50
review:VB 20.00 10.86 10.00   5.28 15.50
a:DT   0.00 20.00 20.00 20.00 20.50
judge:NN 20.00   2.20 15.00   7.55   7.06
order:NN 20.00   5.40 15.00 10.51   7.78
approving:VBG 20.00 13.30 10.00   7.91 15.50
class-action:JJ 20.00   9.76 12.00   9.63 12.50
status:NN 20.00   6.67 15.00 12.91   6.58
a:DT   0.00 20.00 20.00 20.00 20.50
sex-discrimination:JJ 20.00   8.36 12.00   8.42 12.50
lawsuit:NN 20.00   0.00 15.00   6.65   7.55
NO_WORD   1.00 10.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.15 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "sex-discrimination" of "lawsuit" dropped on aligned hyp word "lawsuit"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-0.05  1.00 NullPunisher.aux : was
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "filed" aligned badly to "approving"
-1.00  1.00 Structure.relMismatch : text "lawsuit" is prep_for of "approving" while hyp "lawsuit" is nsubjpass of "filed" which aligned to text "approving"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.1357
Threshold: -1.8794


Inference ID: 1005

Txt: Jessica Litman, a law professor at Michigan's Wayne State University, has specialized in copyright law and Internet law for more than 20 years.

Hyp: Jessica Litman is a law professor. (yes)

Jessica_Litman
NNP
is
VBZ
a
DT
law
NN
professor
NN
Jessica_Litman:NNP   0.00 15.50 20.50 10.50 10.50
a:DT 20.50 20.00   0.00 20.00 20.00
law:NN 10.50 15.00 20.00   0.00   4.91
professor:NN 10.50 15.00 20.00   4.91   0.00
Michigan:NNP 10.50 15.50 20.50   9.35   9.49
Wayne_State_University:NNP 10.50 15.50 20.50   9.57   8.91
has:VBZ 15.50   8.64 20.00 15.00 15.00
specialized:VBN 15.50 10.00 20.00 13.33 13.82
copyright:NN 10.50 15.00 20.00   5.62   8.51
law:NN 10.50 15.00 20.00   0.00   4.91
Internet:NN 10.50 15.00 20.00   8.30   8.36
law:NN 10.50 15.00 20.00   0.00   4.91
more_than:IN 20.50 20.00 20.00 20.00 20.00
20:CD 20.50 20.50 20.50 19.94 19.73
years:NNS 10.50 15.00 20.00   6.81   8.28
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  2.03 Alignment.score
 1.00  0.95 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Wayne_State_University" of "professor" dropped on aligned hyp word "professor"
-0.05  1.00 NullPunisher.aux : is
 1.00  1.00 Quant.contract : [a,a]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Jessica_Litman" <-nsubj-- "specialized" vs. hyp "Jessica_Litman" <-nsubj-- "professor", which aligned to text "professor" args have different parents, different relations: text "law" <-appos-- "Jessica_Litman" vs. hyp "law" <-nn-- "professor", which aligned to text "professor"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -0.7845
Threshold: -1.8794


Inference ID: 2132

Txt: Ralph Fiennes, who has played memorable villains in such films as 'Red Dragon' and 'Schindler's List,' is to portray Voldemort, the wicked warlock, in the next Harry Potter movie.

Hyp: Ralph Fiennes will play Harry Potter in the next movie. (don't know)

Ralph_Fiennes
NNP
will
MD
play
VB
Harry_Potter
NNP
the
DT
next
JJ
movie
NN
Ralph_Fiennes:NNP   0.50 20.50 15.50 10.00 20.50 12.50 10.50
who:WP 12.50 20.00 15.00 12.50 20.00 15.00 12.00
has:VBZ 15.50 20.00 10.00 15.50 20.00 12.00 15.00
played:VBN 15.50 20.00   0.00 15.50 20.00 12.00 12.85
memorable:JJ 12.50 20.00   8.68 12.50 20.00 10.00   5.86
villains:NNS 10.50 20.00 13.22   9.06 20.00 12.00   4.26
such:JJ 12.50 20.00 12.00 12.50 20.00 10.00 12.00
films:NNS 10.50 20.00 12.92   9.84 20.00 12.00   0.00
Red_Dragon:NNP 10.00 20.50 15.50 10.07 20.50 12.50   8.36
`:`` 20.50 10.00 20.00 20.50 10.00 20.00 19.33
Schindler:NNP 10.50 20.00 15.00 10.50 20.00 12.00 10.00
List:NN 10.50 20.00 15.00   9.54 20.00 12.00   6.02
is:VBZ 15.50 20.00 10.00 15.50 20.00 12.00 15.00
to:TO 20.50 10.00 20.00 20.50 10.00 20.00 20.00
portray:VB 15.50 20.00   8.26 15.50 20.00 12.00 11.49
Voldemort:NNP 10.00 20.50 15.50 10.50 20.50 12.50 10.50
the:DT 20.50 10.00 20.00 20.50   0.00 20.00 20.00
wicked:JJ 12.50 20.00 10.58 12.50 20.00 10.00   7.68
warlock:NN 10.50 20.00 15.00 10.50 20.00 12.00   8.93
the:DT 20.50 10.00 20.00 20.50   0.00 20.00 20.00
next:JJ 12.50 20.00 12.00 12.50 20.00   0.00 12.00
Harry_Potter:NNP 10.50 20.50 15.50   0.00 20.50 12.50   9.84
movie:NN 10.50 20.00 13.01   9.84 20.00 12.00   0.00
NO_WORD 10.00 10.00 10.00 10.00   1.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.57 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.57 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Red_Dragon" of "played" dropped on aligned hyp word "play"
-0.05  1.00 NullPunisher.aux : will
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Harry_Potter" <-prep_in-- "Voldemort" vs. hyp "Harry_Potter" <-dobj-- "play", which aligned to text "played"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.0918
Threshold: -1.8794


Inference ID: 2186

Txt: One of two men "released" early on Saturday Mohammed Dawoud, 19, of Willesden, north-west London will appear before Horseferry Road magistrates' court today charged with possession of forged identity documents, police said.

Hyp: Willesden will appear before Horseferry Road magistrates' court today. (don't know)

Willesden
NNP
will
MD
appear
VB
Horseferry_Road
NNP
magistrates
NNP
court
NN
today
NN
One:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50
two:CD 20.50 20.50 19.88 20.50 20.50 20.50 18.29
men:NNS 10.50 20.00 13.96   7.26   7.18   5.41   7.30
released:VBN 15.50 20.00   6.45 15.50 14.64 15.00 10.99
early:RB 15.50 20.00 17.83 15.50 15.00 15.00 14.57
Saturday:NNP 10.50 20.50 15.50   8.90   9.17   9.14   7.86
Mohammed_Dawoud:NNP 10.00 20.50 15.50   9.13   9.03 10.13 10.18
19:CD 20.50 20.50 20.48 20.50 20.48 20.02 18.47
Willesden:NNP   0.50 20.50 15.50 10.50 10.50 10.50 10.50
north-west:JJ 12.50 20.00 12.00 12.50 12.00 12.00 12.00
London:NNP 10.50 20.50 15.50   7.86   8.23   8.65   8.78
will:MD 20.50   0.00 20.00 20.50 20.00 20.00 20.00
appear:VB 15.50 20.00   0.00 15.50 14.14 13.61 13.95
Horseferry_Road:NNP 10.00 20.50 15.50   0.00   7.46   8.11   8.27
magistrates:NNP 10.50 20.00 14.14   7.46   0.00   6.81   8.10
court:NN 10.50 20.00 13.61   8.11   6.81   0.00   8.06
today:NN 10.50 20.00 13.95   8.27   8.10   8.06   0.00
charged:VBN 15.50 20.00   8.64 15.50 12.32 12.44 15.00
possession:NN 10.50 20.00 14.44   8.16   7.63   6.10   8.11
forged:JJ 12.50 20.00 11.90 12.50 12.00 12.00 11.61
identity:NN 10.50 20.00 13.58   9.20   7.47   8.91   8.51
documents:NNS 10.50 20.00 12.23   6.57   6.54   6.15   5.70
police:NNS 10.50 20.00 14.39   8.18   8.02   6.57   8.12
said:VBD 15.50 20.00 10.00 15.50 14.84 15.00 14.31
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.74 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.71 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "charged" of "today" dropped on aligned hyp word "today"
 0.50  1.00 Factive.unknownPassage : non factive text -- unknown: charged-VBN
-0.05  1.00 NullPunisher.aux : will
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "court" <-prep_before-- "appear vs. hyp "court" <-prep_before-- "today", which aligned to text "today" args have different parents, different relations: text "Willesden" <-dep-- "London" vs. hyp "Willesden" <-nsubj-- "today", which aligned to text "today" args have different parents, different relations: text "appear" <-ccomp-- "said" vs. hyp "appear" <-dep-- "today", which aligned to text "today" args have different parents, different relations: text "court" <-nsubjpass-- "charged" vs. hyp "court" <-prep_before-- "today", which aligned to text "today"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -1.3987
Threshold: -1.8794


Inference ID: 2184

Txt: Scotland Yard has been granted two more days to question nine of the 13 men arrested in co-ordinated anti-terrorism raids across the country last Tuesday.

Hyp: 13 men killed in co-ordinated anti-terrorism raids across the country last Tuesday. (don't know)

13
CD
men
NNS
killed
VBD
co-ordinated
JJ
anti-terrorism
JJ
raids
NNS
the
DT
country
NN
last
JJ
Tuesday
NNP
Scotland_Yard:NNP 20.50 10.50 15.50 12.50 12.50 10.50 20.50 10.50 12.50 10.50
has:VBZ 20.50 15.00 10.00 12.00 12.00 15.00 20.00 15.00 12.00 15.50
been:VBN 20.50 15.00 10.00 12.00 12.00 15.00 20.00 15.00 12.00 15.50
granted:VBN 20.05 15.00 10.00 12.00 10.86 15.00 20.00 12.76 12.00 15.50
two:CD   5.00 19.84 19.32 20.50 20.50 19.99 20.50 20.16 20.50 20.50
more:JJR 20.50 12.00 12.00 10.00 10.00 12.00 20.00 12.00 10.00 12.50
days:NNS 20.50   6.55 14.48 12.00 12.00   8.80 20.00   6.29 12.00   5.61
to:TO 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.50
question:VB 20.50 14.88 10.00 12.00 11.26 15.00 20.00 14.27 12.00 15.50
nine:CD   5.00 19.75 19.23 20.50 19.85 19.74 20.50 20.50 20.50 20.50
the:DT 20.50 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
13:CD   0.00 20.33 20.50 20.50 20.36 20.50 20.50 20.50 20.50 20.50
men:NNS 20.33   0.00 12.00 12.00 12.00   8.60 20.00   5.90 12.00   8.01
arrested:VBN 20.48 11.95   1.74 12.00   9.94   8.41 20.00 14.00 12.00 15.50
co-ordinated:JJ 20.50 12.00 12.00   0.00 10.00 12.00 20.00 12.00 10.00 12.50
anti-terrorism:JJ 20.36 12.00 10.13 10.00   0.00   9.01 20.00 10.52 10.00 12.50
raids:NNS 20.50   8.60   8.83 12.00   9.01   0.00 20.00   8.44 12.00   9.78
the:DT 20.50 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.00 20.50
country:NN 20.50   5.90 13.58 12.00 10.52   8.44 20.00   0.00 12.00   7.80
last:JJ 20.50 12.00 12.00 10.00 10.00 12.00 20.00 12.00   0.00 12.50
Tuesday:NNP 20.50   8.01 15.50 12.50 12.50   9.78 20.50   7.80 12.50   0.00
NO_WORD 10.00 10.00 10.00   9.00   9.00 10.00   1.00 10.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.76 Alignment.score
 1.00  0.94 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  10.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1000
-1.00  1.00 Structure.relMismatch : text "men" is nsubjpass of "arrested" while hyp "men" is nsubj of "killed" which aligned to text "arrested"
Hand-tuned score (dot product of above): 3.2954
Threshold: -1.8794


Inference ID: 928

Txt: Suncreams designed for children could offer less protection than they claim on the bottle.

Hyp: Suncreams designed for children protect at the level they advertise. (don't know)

Suncreams
NNP
designed
VBN
children
NNS
protect
VBP
the
DT
level
NN
children
NNS
advertise
VBP
Suncreams:NNP   0.00 15.50 10.50 15.50 20.50 10.50 10.50 15.50
designed:VBN 15.50   0.00 13.68   8.55 20.00 14.58 13.68   8.81
children:NNS 10.50 13.68   0.00 12.23 20.00   7.89   0.00 14.89
could:MD 20.50 20.00 20.00 20.00 10.00 20.00 20.00 20.00
offer:VB 15.50   9.38 14.60   9.68 20.00 14.92 14.60   7.93
less:JJR 12.50 12.00 12.00 12.00 20.00 12.00 12.00 12.00
protection:NN 10.50 13.83   7.80   1.00 20.00   7.09   7.80 14.43
than:IN 20.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00
children:NNS 10.50 13.68   0.00 12.23 20.00   7.89   0.00 14.89
claim:VBP 15.50   9.37 15.00   6.77 20.00 15.00 15.00   7.79
the:DT 20.50 20.00 20.00 20.00   0.00 20.00 20.00 20.00
bottle:NN 10.50 15.00   8.69 15.00 20.00   8.67   8.69 13.24
NO_WORD 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.39 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.38 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added advertise[advertise-VBP]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "less" of "protection" dropped on aligned hyp word "protect"
-1.00  1.00 NullPunisher.other : advertise
-1.00  1.00 NullPunisher.other : level
-0.10  1.00 NullPunisher.article : the
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Suncreams" <-nsubj-- "offer" vs. hyp "Suncreams" <-nsubj-- "protect", which aligned to text "protection" args have different parents, different relations: text "Suncreams" <-nsubjpass-- "designed" vs. hyp "Suncreams" <-nsubj-- "protect", which aligned to text "protection"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -5.6618
Threshold: -1.8794


Inference ID: 916

Txt: Medicare will begin paying later this year for certain oral cancer drugs and intravenous drugs that can be self-administered under a $500 million demonstration project unveiled yesterday.

Hyp: Medicare will start paying users $500 million. (don't know)

Medicare
NNP
will
MD
start
VB
paying
CD
users
NNS
$
$
500
CD
million
CD
Medicare:NNP   0.00 20.50 15.50 20.50   9.30 20.50 20.50 20.50
will:MD 20.50   0.00 20.00 20.50 20.00 10.50 20.50 20.50
begin:VB 15.50 20.00   2.69 20.50 14.22 20.50 20.50 18.50
paying:VBG 15.50 20.00   8.85   0.50 13.86 17.93 19.83 18.62
later:RBR 15.50 20.00 16.06 19.93 14.94 20.50 19.97 19.29
this:DT 20.50 10.00 20.00 20.50 20.00 10.50 20.50 20.50
year:NN   9.16 20.00 14.65 20.37   6.84 17.16 19.50 18.60
certain:JJ 12.50 20.00 11.33 17.85 11.63 20.50 20.50 18.36
oral_cancer:NNS 10.50 20.00 15.00 20.50 10.00 20.50 20.50 20.50
drugs:NNS   9.25 20.00 15.00 20.50   5.53 20.50 19.89 20.50
intravenous:JJ 12.50 20.00 12.00 20.50 10.27 20.23 20.50 20.50
drugs:NNS   9.25 20.00 15.00 20.50   5.53 20.50 19.89 20.50
that:WDT 20.50 10.00 20.00 20.50 20.00 10.50 20.50 20.50
can:MD 20.50 10.00 20.00 20.50 20.00 10.50 20.50 20.50
be:VB 15.50 20.00 10.00 20.50 15.00 20.50 20.50 20.50
self-administered:VBN 15.50 20.00   9.89 20.50 14.25 20.50 20.50 19.48
a:DT 20.50 10.00 20.00 20.50 20.00 10.50 20.50 20.50
$:$ 20.50 10.50 20.50 17.93 20.37   0.00 18.94 16.63
500:CD 20.50 20.50 19.58   9.83 19.76 18.94   0.00   0.00
million:CD 20.50 20.50 19.00   8.62 20.21 16.63   0.00   0.00
demonstration:NN   7.98 20.00 12.67 20.38   6.96 20.18 19.91 20.50
project:NN   7.57 20.00 14.39 20.50   6.50 18.88 19.07 20.28
unveiled:VBD 15.50 20.00   6.63 20.29 12.83 19.23 20.50 20.50
yesterday:NN   9.47 20.00 15.00 20.50   7.36 20.12 20.50 20.50
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.88 Alignment.score
 1.00  0.87 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  3.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.38 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added $[$-$]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "drugs" of "paying" dropped on aligned hyp word "paying"
-3.00  1.00 NullPunisher.entity : $
-0.05  1.00 NullPunisher.aux : will
-1.00  1.00 NullPunisher.other : users
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.3688
Threshold: -1.8794


Inference ID: 2183

Txt: One of the dead was a child, passing by with his parents, said Iqrar Abbasi, a doctor at Civil Hospital Karachi.

Hyp: A doctor was killed by his parents. (don't know)

A
DT
doctor
NN
was
VBD
killed
VBN
his
PRP$
parents
NNS
One:CD 20.50 20.50 20.50 20.50 20.50 20.50
the:DT 10.00 20.00 20.00 20.00 20.00 20.00
dead:NN 20.00   6.08 15.00   8.10 12.00   9.23
was:VBD 20.00 15.00   0.00 10.00 15.00 15.00
a:DT   0.00 20.00 20.00 20.00 20.00 20.00
child:NN 20.00   3.39 15.00 14.01 12.00   2.76
passing:VBG 20.00 15.00 10.00   9.32 15.00 14.87
by:IN 20.00 20.00 20.00 20.00 20.00 20.00
his:PRP$ 20.00 12.00 15.00 15.00   0.00 12.00
parents:NNS 20.00   6.31 15.00 14.69 12.00   0.00
said:VBD 20.00 15.00 10.00   8.97 15.00 15.00
Iqrar_Abbasi:NNP 20.50 10.50 15.50 15.50 12.50 10.50
a:DT   0.00 20.00 20.00 20.00 20.00 20.00
doctor:NN 20.00   0.00 15.00 14.14 12.00   6.31
Civil_Hospital_Karachi:NNP 20.50   7.62 15.50 15.50 12.50   8.16
NO_WORD   1.00 10.00   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.49 Alignment.score
 1.00  0.92 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "Civil_Hospital_Karachi" of "doctor" dropped on aligned hyp word "doctor"
-0.05  1.00 NullPunisher.aux : was
-1.00  1.00 NullPunisher.other : killed
 1.00  1.00 Quant.contract : [a,a]
-2.00  1.00 RootEntailment.unalignedRoot : "killed" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.3984
Threshold: -1.8794


Inference ID: 908

Txt: Time Warner is the world's largest media and Internet company.

Hyp: Time Warner is the world's largest company. (don't know)

Time_Warner
NNP
is
VBZ
the
DT
world
NN
largest
JJS
company
NN
Time_Warner:NNP   0.00 15.50 20.50   8.16 12.50   7.30
is:VBZ 15.50   0.00 20.00 15.00 12.00 15.00
the:DT 20.50 20.00   0.00 20.00 20.00 20.00
world:NN   8.16 15.00 20.00   0.00   5.56   5.51
largest:JJS 12.50 12.00 20.00   5.56   0.00 11.67
media:NNS   7.62 15.00 20.00   7.30 12.00   6.37
Internet:NN   8.56 15.00 20.00   8.20 12.00   7.46
company:NN   7.30 15.00 20.00   5.51 11.67   0.00
NO_WORD 10.00   1.00   1.00 10.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.64 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.00  1.00 NegPolarity.hypNegWord : "largest": tag "JJS" is in NegPolarityMarkers list
 0.00  1.00 NegPolarity.txtNegWord : "largest": tag "JJS" is in NegPolarityMarkers list
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "company" aligned badly to "media"
 0.00  1.00 NegPolarity.txtNegWord&NegPolarity.hypNegWord :
Hand-tuned score (dot product of above): 1.2752
Threshold: -1.8794


Inference ID: 975

Txt: After watching fireworks yesterday evening in Cedar Rapids, Kerry went to his wife's suburban Pittsburgh farm, where he was expected to hold a barbecue for supporters this afternoon.

Hyp: Kerry watched fireworks. (yes)

Kerry
NNP
watched
VBD
fireworks
NNS
After:IN 20.00 20.00 20.00
watching:VBG 15.00   0.00 10.24
fireworks:NNS 10.00 11.15   0.00
yesterday:NN 10.00 14.54   8.88
evening:NN 10.00 11.20   5.31
Cedar_Rapids:NNP 10.50 15.50 10.50
Kerry:NNP   0.50 15.50 10.50
went:VBD 15.00   6.34 14.92
his:PRP$ 12.00 15.00 12.00
wife:NN 10.00 14.45   8.85
suburban:JJ 12.00 10.62 10.60
Pittsburgh:NNP 10.50 15.50   9.72
farm:NN 10.00 14.41   8.99
where:WRB 20.00 20.00 20.00
his:PRP$ 12.00 15.00 12.00
was:VBD 15.00 10.00 15.00
expected:VBN 15.00   8.78 12.27
to:TO 20.00 20.00 20.00
hold:VB 15.00   8.47 13.28
a:DT 20.00 20.00 20.00
barbecue:NN 10.00 14.39   6.17
supporters:NNS 10.00 14.59   8.58
this:DT 20.00 20.00 20.00
afternoon:NN 10.00 12.24   7.07
NO_WORD 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.55 Alignment.score
 1.00  0.93 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "yesterday" of "watching" dropped on aligned hyp word "watched"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "Kerry" <-nsubj-- "went" vs. hyp "Kerry" <-nsubj-- "watched", which aligned to text "watching"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -2.1594
Threshold: -1.8794


Inference ID: 912

Txt: Vice President Dick Cheney on Tuesday hurled an obscenity on the Senate floor to punctuate an angry exchange with Vermont Sen. Patrick Leahy as all senators gathered for their annual photo.

Hyp: Cheney cursed at Sen. Patrick Leahy. (yes)

Cheney
NNP
cursed
VBD
Sen.
NNP
Patrick_Leahy
NNP
Vice_President:NNP 10.00 15.00   9.58   9.16
Dick_Cheney:NNP   5.50 15.50   9.75   4.50
Tuesday:NNP 10.50 15.50   7.75   9.71
hurled:VBN 15.00   3.97 15.00 15.50
an:DT 20.00 20.00 20.00 20.50
obscenity:NN 10.00 14.01   9.02 10.33
the:DT 20.00 20.00 20.00 20.50
Senate:NNP 10.50 15.50   5.50   9.67
floor:NN 10.00 14.14   8.79   9.45
to:TO 20.00 20.00 20.00 20.50
punctuate:VB 15.00   8.18 15.00 15.50
an:DT 20.00 20.00 20.00 20.50
angry:JJ 12.00   6.93 12.00 12.50
exchange:NN 10.00 15.00   8.21   9.38
Vermont:NNP 10.50 15.50   9.65   9.83
Sen.:NNP 10.00 15.00   0.00   9.79
Patrick_Leahy:NNP 10.50 15.50   9.79   0.00
all:DT 20.00 20.00 20.00 20.50
senators:NNS 10.00 15.00   8.83   8.87
gathered:VBD 15.00   6.28 15.00 15.50
their:PRP$ 12.00 15.00 12.00 12.50
annual:JJ 12.00 12.00 12.00 12.50
photo:NN 10.00 14.81   8.64   9.32
NO_WORD 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.22 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "senators" of "Patrick_Leahy" dropped on aligned hyp word "Patrick_Leahy"
-1.00  1.00 NullPunisher.other : cursed
-2.00  1.00 RootEntailment.unalignedRoot : "cursed" not aligned to anything
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 0.2765
Threshold: -1.8794


Inference ID: 331

Txt: Chancellor Schroeder has presided over three years of almost zero growth in the economy and an unemployment rate that has remained stubbornly above four million people.

Hyp: More than four million people have remained stubbornly unemployed in the last three years. (don't know)

More_than
IN
four
CD
million
CD
people
NNS
have
VBP
remained
VBN
stubbornly
RB
unemployed
JJ
the
DT
last
JJ
three
CD
years
NNS
Chancellor:NNP 20.00 20.50 20.50   7.71 15.00 15.00 15.00 12.00 20.00 12.00 20.50   8.37
Schroeder:NNP 20.50 20.50 20.50 10.50 15.50 15.50 15.50 12.50 20.50 12.50 20.50 10.50
has:VBZ 20.00 20.50 20.50 15.00   0.00 10.00 20.00 12.00 20.00 12.00 20.50 15.00
presided:VBN 20.00 18.05 20.50 15.00 10.00   7.35 16.12 11.33 20.00 12.00 18.47 11.65
three:CD 20.50   0.22   5.00 20.50 20.50 18.64 19.64 20.35 20.50 20.50   0.00 16.91
years:NNS 20.00 16.38 20.50   6.24 15.00 13.83 13.78 11.08 20.00 12.00 16.91   0.00
almost:RB 20.00 20.50 20.50 15.00 20.00 20.00 10.00 12.00 20.00 12.00 20.50 15.00
zero:CD 20.50   5.00   5.00 20.50 20.50 20.50 19.79 20.50 20.50 20.50   5.00 18.73
growth:NN 20.00 20.33 20.04   6.55 15.00 13.49 13.30 10.55 20.00 12.00 19.93   7.41
the:DT 20.00 20.50 20.50 20.00 20.00 20.00 20.00 20.00   0.00 20.00 20.50 20.00
economy:NN 20.00 20.50 20.50   5.01 15.00 15.00 13.29   9.79 20.00 12.00 20.50   7.45
an:DT 20.00 20.50 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
unemployment_rate:NN 20.00 20.50 20.50 10.00 15.00 15.00 15.00   7.00 20.00 12.00 20.50 10.00
that:WDT 20.00 20.50 20.50 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.50 20.00
has:VBZ 20.00 20.50 20.50 15.00   0.00 10.00 20.00 12.00 20.00 12.00 20.50 15.00
remained:VBN 20.00 18.71 20.50 15.00 10.00   0.00 15.05   9.58 20.00 12.00 18.64 13.83
stubbornly:RB 20.00 19.57 20.50 15.00 20.00 15.05   0.00   9.57 20.00 12.00 19.64 13.78
four:CD 20.50   0.00   0.00 20.50 20.50 18.71 19.57 20.50 20.50 20.50   0.22 16.38
million:CD 20.50   0.00   0.00 19.39 20.50 20.50 20.50 20.01 20.50 20.50   5.00 20.50
people:NNS 20.00 20.50 19.39   0.00 15.00 15.00 15.00   8.03 20.00 12.00 20.50   6.24
NO_WORD 10.00 10.00 10.00 10.00   1.00   1.00   9.00   9.00   1.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.17 Alignment.score
 1.00  0.90 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  5.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : hyp added last[last-JJ]
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "growth" of "years" dropped on aligned hyp word "years"
-1.00  1.00 NullPunisher.other : More_than
-0.05  1.00 NullPunisher.aux : have
-1.00  1.00 NullPunisher.other : remained
-1.00  1.00 NullPunisher.other : last
-0.10  1.00 NullPunisher.article : the
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '3.0' vs '>3.0'
-2.00  1.00 RootEntailment.poorlyAlignedRoot : "unemployed" aligned badly to "unemployment_rate"
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "stubbornly" <-advmod-- "remained" vs. hyp "stubbornly" <-advmod-- "unemployed", which aligned to text "unemployment_rate" args have different parents, different relations: text "people" <-prep_above-- "remained" vs. hyp "people" <-nsubj-- "unemployed", which aligned to text "unemployment_rate" args have different parents, different relations: text "years" <-prep_over-- "presided" vs. hyp "years" <-prep_in-- "unemployed", which aligned to text "unemployment_rate"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -15.2456
Threshold: -1.8794


Inference ID: 2181

Txt: Two bombs planted near an Islamic school in Pakistan killed eight people and injured 42 others yesterday in the latest outbreak of violence gripping the southern port of Karachi.

Hyp: 42 people killed in Karachi bomb attack. (don't know)

42
CD
people
NNS
killed
VBD
Karachi
NNP
bomb
NN
attack
NN
Two:CD   5.00 20.50 20.50 20.50 20.50 20.50
bombs:NNS 20.50   6.80   9.04 10.50   0.00   4.30
planted:VBN 20.12 14.27   7.39 15.50 12.64 13.42
an:DT 20.50 20.00 20.00 20.50 20.00 20.00
Islamic:JJ 20.50 12.00 12.00 12.50 12.00 12.00
school:NN 19.83   5.48 15.00 10.50   8.17   8.03
Pakistan:NNP 20.50   8.75 15.50 10.00   9.84   9.49
killed:VBD 20.50 11.02   0.00 15.50   9.63   9.17
eight:CD   5.00 20.50 19.03 20.50 19.92 20.50
people:NNS 20.50   0.00 11.02 10.50   6.80   6.61
injured:VBD 20.50 12.35   3.02 15.50 11.11 10.10
42:CD   0.00 20.50 20.50 20.50 20.42 20.50
others:NNS 20.50   7.74 15.00 10.50 10.00 10.00
yesterday:NN 19.46   6.54 15.00 10.50   8.76   7.70
the:DT 20.50 20.00 20.00 20.50 20.00 20.00
latest:JJS 19.51 12.00 12.00 12.50 11.72 10.82
outbreak:NN 20.50   7.95 10.18 10.50   5.65   5.20
violence:NN 19.65   7.02 10.75 10.50   7.25   5.83
gripping:VBG 20.46 15.00 10.00 15.50 15.00 14.60
the:DT 20.50 20.00 20.00 20.50 20.00 20.00
southern:JJ 20.50 12.00 12.00 12.50 12.00 11.51
port:NN 20.42   6.48 14.64 10.50   6.26   7.64
Karachi:NNP 20.50 10.50 15.50   0.00 10.50 10.50
NO_WORD 10.00 10.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  1.09 Alignment.score
 1.00  0.89 Alignment.isGood
-1.00  0.00 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  6.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : text adjunct "school" of "planted" dropped on aligned hyp word "attack"
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '42.0' vs '8.0'
-1.00  1.00 Structure.relMismatch : text "people" is dobj of "killed" while hyp "people" is nsubj of "killed" which aligned to text "killed"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): -3.3756
Threshold: -1.8794


Hand-set weights Accuracy: 154/280 = 0.5500


Word similarity table built on Sun Jan 07 21:50:15 PST 2007 using command:
java edu.stanford.nlp.rte.WordSimilarityGenerator -info /u/nlp/rte/data/byformat/align/stochastic/RTE1_dev2.align.xml -output /u/nlp/rte/data/byformat/wordsim/stochastic/RTE1_dev2.wordsim.html -aligner.weights /u/nlp/rte/resources/align/perceptron_weights.txt