Txt/Hyp term similarities

The rows are the txt words. The columns are hyp words.

Lexical resource summary:
Acronym: AcronymLexicalResource
BasicWN: null
Country: CountryLexicalResource
Coref: CorefLexicalResource
Cyc: null
DekangLin: DekangLinLexicalResource
EditDistance: EditDistanceLexicalResource
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
WNAntonymy: WNAntonymyLexicalResource
WNHypernymy: WNHypernymyLexicalResource
WNSynonymy: WNSynonymyLexicalResource


Inference ID: 754

Txt: Mexico City has a very bad pollution problem because the mountains around the city act as walls and block in dust and smog.

Hyp: Poor air circulation out of the mountain-walled Mexico City aggravates pollution. (yes)

Poor
NNP		 air
NN		 circulation
NN		 the
DT		 mountain-walled
JJ		 Mexico_City
NN		 aggravates
VBZ		 pollution
NN
Mexico_City:NNP 10.50 10.50   9.59 20.50 12.50   0.00 15.50 10.50
has:VBZ 15.00 15.00 15.00 20.00 12.00 15.50   7.46 15.00
a:DT 20.00 20.00 20.00 10.00 20.00 20.50 20.00 20.00
very:RB 15.00 13.57 15.00 20.00 12.00 15.50 20.00 15.00
bad:JJ 12.00 12.00   8.95 20.00 10.00 12.50 11.57 10.68
pollution:NN   9.23   5.73   6.00 20.00 10.33 10.50 14.05   0.00
problem:NN   8.53   8.24   8.66 20.00 11.75 10.50 14.41   5.74
the:DT 20.00 20.00 20.00   0.00 20.00 20.50 20.00 20.00
mountains:NNS   9.24   7.38   9.00 20.00   7.00   9.50 13.42   6.03
the:DT 20.00 20.00 20.00   0.00 20.00 20.50 20.00 20.00
city:NN   8.60   7.65   8.75 20.50   8.21   9.33 13.54   7.48
act:NN   8.56   6.67   8.69 20.00 12.00 10.50 12.87   7.19
walls:NNS   8.88   8.16   8.99 20.00   7.65 10.50 14.71   8.72
block:NN   8.81   7.30   8.93 20.00 11.34 10.50 13.99   9.01
dust:NN   9.47   6.49   9.50 20.00   9.41 10.50 15.00   3.40
smog:NN   7.50   6.04   7.67 20.00 11.08 10.50 15.00   2.50
NO_WORD 10.00 10.00 10.00   1.00   9.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -6.15 Alignment.score
 1.00  0.01 Alignment.isGood
-1.00  0.84 Alignment.isBad
 0.10  3.00 Alignment.hypSpan
 0.10  0.25 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "mountain-walled" modifying "Mexico_City"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "mountains" modifying "has" is dropped on aligned hypothesis word "aggravates"
-1.00  1.00 Hypernym.posNarrow : Narrowing a term (from "act" to "circulation") does NOT preserve truth in a positive context
-0.10  1.00 NullPunisher.functionWord : the
-4.00  1.00 NullPunisher.other : mountain-walled
 1.00  1.00 WorldKnowledge.match : Location match: "Mexico_City" entails "mexico_city"
Hand-tuned score (dot product of above): -11.2618
Threshold: -1.1456

Inference ID: 822

Txt: Satomi Mitarai died of blood loss.

Hyp: Satomi Mitarai bled to death. (yes)

Satomi_Mitarai
NNP		 bled
VBD		 death
NN
Satomi_Mitarai:NNP   0.00 15.50 10.50
died:VBD 15.50   5.00   2.50
blood:NN 10.50   9.44   5.18
loss:NN 10.50 13.42   1.25
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -2.42 Alignment.score
 1.00  0.23 Alignment.isGood
-1.00  0.11 Alignment.isBad
 0.10  3.00 Alignment.hypSpan
 0.10  0.67 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "blood" modifying "loss" is dropped on aligned hypothesis word "death"
 1.00  1.00 Hypernym.posWiden : Widening a term (from "loss" to "death") preserves truth in a positive context
Hand-tuned score (dot product of above): -0.4322
Threshold: -1.1456

Inference ID: 692

Txt: The national insurrection of 1794, led by Tadeusz Kosciuszko against the Russo-Prussian second partition, was brutally crushed; the ensuing third partition of Poland among Russia, Prussia, and Austria left Warsaw a provincial town of South Prussia.

Hyp: Warsaw became a town in Prussia. (yes)

Warsaw
NNP		 became
VBD		 a
DT		 town
NN		 Prussia
NNP
The:DT 20.50 20.00 10.00 18.57 20.50
national:JJ 11.07 12.00 20.00 11.61 11.83
insurrection:NN 10.18 15.00 20.00   8.49 10.39
1794:CD 20.50 20.50 20.50 19.55 20.50
led:VBN 15.50   6.25 20.00 15.00 15.50
Tadeusz_Kosciuszko:NNP 10.50 15.50 20.50 10.50 10.50
the:DT 20.50 20.00 10.00 18.57 20.50
Russo-Prussian:JJ 12.50 12.50 20.50 12.50   7.50
second:JJ 12.50   9.17 20.50 12.50 12.50
partition:NN   9.32 15.00 20.00   7.63   8.00
was:VBD 12.17   4.02 20.00 15.00 15.50
brutally:RB 15.50 18.57 20.00 14.27 13.50
crushed:VBN 14.73   7.18 20.00 14.47 11.21
the:DT 20.50 20.00 10.00 18.57 20.50
ensuing:VBG 15.50   6.54 20.00 15.00 14.07
third:JJ 12.50 12.50 20.50 11.39 12.50
partition:NN   9.32 15.00 20.00   7.63   8.00
Poland:NNP   7.13 13.83 20.50   6.93   7.57
Russia:NNP   7.35 15.50 20.50   7.26   1.54
Prussia:NNP   7.71 15.50 20.50   6.90   0.00
Austria:NNP   7.59 15.50 20.50   7.62   5.71
left:VBD 15.50   6.14 20.00 13.66 15.50
Warsaw:NNP   0.00 15.50 20.50   5.11   7.71
a:DT 20.50 20.00   0.00 20.00 20.50
provincial:JJ 12.50 12.00 20.00 10.25   9.56
town:NN   5.11 15.00 20.00   0.00   6.90
South_Prussia:NNP   6.96 15.50 20.50   5.41   5.00
NO_WORD 10.00   1.00   1.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.60 Alignment.score
 1.00  0.65 Alignment.isGood
-1.00  0.02 Alignment.isBad
 0.10  3.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "South_Prussia" modifying "town" is dropped on aligned hypothesis word "town"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
-4.00  0.21 NullPunisher.other : became
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Warsaw"
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Prussia"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.5320
Threshold: -1.1456

Inference ID: 731

Txt: The city Tenochtitlan grew rapidly and was the center of the Aztec's great empire.

Hyp: Tenochtitlan quickly spread over the island, marshes, and swamps. (don't know)

Tenochtitlan
NNP		 quickly
RB		 spread
VBD		 the
DT		 island
NN		 marshes
NNS		 swamps
NNS
The:DT 20.50 20.00 20.00   0.00 20.00 20.00 20.00
city:NN 10.50 14.09 15.00 20.00   5.34   8.06   7.98
Tenochtitlan:NN   0.00 15.50 15.50 20.50 10.50 10.50 10.50
grew:VBD 15.50 20.00   7.75 18.57 13.96 14.09 13.66
rapidly:RB 15.50   0.00 19.23 20.00 12.79 13.31 13.00
was:VBD 15.50 20.00   5.33 20.00 15.00 15.00 11.67
the:DT 20.50 20.00 20.00   0.00 20.00 20.00 20.00
center:NN 10.50 14.83 14.61 20.00   6.77   7.97   9.13
the:DT 20.50 20.00 20.00   0.00 20.00 20.00 20.00
Aztec:NN 10.50 15.50 14.59 20.50 10.50   8.83 10.50
great:JJ 12.50 11.52   7.45 20.00 11.09 10.61 10.40
empire:NN 10.50 15.00 13.33 20.00   4.93   7.21   7.58
NO_WORD 10.00   9.00 10.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -5.10 Alignment.score
 1.00  0.02 Alignment.isGood
-1.00  0.65 Alignment.isBad
 0.10  3.00 Alignment.hypSpan
 0.10  0.43 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "swamps" modifying "spread"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "empire" is dropped on aligned hypothesis word "island"
-4.00  0.74 NullPunisher.other : swamps
-4.00  0.75 NullPunisher.other : marshes
-0.10  1.00 NullPunisher.functionWord : the
Hand-tuned score (dot product of above): -11.9354
Threshold: -1.1456

Inference ID: 1865

Txt: Militant groups in Iraq have waged a kidnapping campaign aimed at driving out U.S. supporting companies and troops.

Hyp: About 70 foreigners have been abducted by militants fighting the U.S.-led occupation and reconstruction efforts in Iraq. (don't know)

About
RB		 70
CD		 foreigners
NNS		 have
VBP		 been
VBN		 abducted
VBN		 militants
NNS		 fighting
VBG		 the
DT		 U.S.-led
JJ		 occupation
NN		 reconstruction
NN		 efforts
NNS		 Iraq
NNP
Militant:JJ 12.00 12.50 12.00 12.00 12.00 12.00   2.00   8.25 20.00 10.50 12.00 12.00 12.00 12.50
groups:NNS 12.27 20.50   6.47 15.00 15.00 13.33   6.73 13.91 20.00 10.00   3.81   8.49   4.07   8.59
Iraq:NNP 15.50 20.50   9.70 15.50 15.50 15.50   9.76 15.50 20.50 12.50   9.29 10.39   9.39   0.00
have:VBP 20.00 20.50 15.00   0.00   3.06   8.01 15.00   5.31 18.57 12.50 15.00 15.00 15.00 15.50
waged:VBD 20.00 19.71 15.00   5.99   5.13   8.61 12.15   1.08 20.00 10.38 11.86 13.74 11.86 15.50
a:DT 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.50 20.00 20.00 20.00 20.50
kidnapping:NN 15.00 20.50   7.96 15.00 15.00   8.05   5.79 11.67 20.00 12.18   7.41   9.72   7.48 10.39
campaign:NN 15.00 20.50   8.80 15.00 15.00 15.00   9.18 13.75 20.00 12.01   5.56   9.20   0.00 10.07
aimed:VBN 18.00 20.50 13.20   5.36   4.43   7.69 14.26   6.38 20.00 11.48 15.00 14.13   9.00 14.39
driving_out:VBG 20.00 18.77 13.55   7.59   6.95   7.83 14.00   8.12 20.00 11.99 14.52 13.80 15.00 15.50
U.S.:NNP 15.50 20.50   8.59 15.50 15.50 15.50   9.11 15.50 20.50   7.50   6.67   9.90   6.87   8.47
supporting:VBG 20.00 20.40 13.49   5.19   4.24   8.55 14.47   6.24 20.00 11.14 13.00 14.15 10.16 15.50
companies:NNS 15.00 20.50   5.49 15.00 15.00 14.41   7.78 14.41 20.00 10.63   5.64   9.22   5.85   9.44
troops:NNS 14.09 20.50   8.48 15.00 15.00 14.02   5.84   9.90 20.00 12.50   3.71   7.66   7.37 10.22
NO_WORD   9.00 10.00 10.00   1.00   1.00 10.00 10.00 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 -4.97 Alignment.score
 1.00  0.02 Alignment.isGood
-1.00  0.62 Alignment.isBad
 0.10  4.00 Alignment.hypSpan
 0.10  0.29 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "U.S.-led" modifying "occupation"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "aimed" modifying "campaign" is dropped on aligned hypothesis word "efforts"
 1.00  1.00 Hypernym.posWiden : Widening a term (from "wage" to "fight") preserves truth in a positive context
-0.10  0.50 NullPunisher.functionWord : have
-0.10  1.00 NullPunisher.functionWord : the
-4.00  0.13 NullPunisher.other : About
-3.00  1.00 NullPunisher.entity : 70
-3.00  1.00 NullPunisher.entity : U.S.-led
-0.10  0.50 NullPunisher.functionWord : been
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Iraq"
Hand-tuned score (dot product of above): -10.3372
Threshold: -1.1456

Inference ID: 864

Txt: Massoud Emami, governor of Iran's northwestern province of Qazvin, and at least 8 others on abroad a helicopter were killed in a crash.

Hyp: Governor of Iran's Qazvin province, Massoud Emami, and eight others were killed (yes)

Governor
NNP		 Iran
NNP		 Qazvin
JJ		 province
NN		 Massoud_Emami
NNP		 eight
CD		 others
NNS		 were
VBD		 killed
VBN
Massoud_Emami:RB 15.50 15.50 12.50 15.50   0.00 20.50 15.50 20.50 20.50
governor:NN   0.00   9.68 12.50   7.50 10.50 20.40   8.57 15.00 15.00
Iran:NNP   9.68   0.00 12.00   6.06 10.50 19.39 10.50 15.50 15.50
northwestern:NN   9.00 10.50 12.50   8.29 10.50 19.69   7.78 15.00 15.00
province:NN   7.50   6.06   9.64   0.00 10.50 20.11 10.00 15.00 13.15
Qazvin:NNP 10.50 10.00   0.00   7.64 10.50 20.50 10.50 15.50 15.50
at:IN 20.00 20.50 20.50 20.00 20.50 20.50 20.00 20.00 20.00
least:JJS 12.00 11.39 10.50 12.00 12.50 10.50 11.09 10.89 12.00
8:CD 20.50 20.50 12.50 19.20 20.50   0.00 20.50 20.50 20.50
others:NNS   8.57 10.50 12.50 10.00 10.50 20.50   0.00 13.00 15.00
abroad:RB 15.00 13.50 12.50 15.00 15.50 20.50 15.00 20.00 18.33
a:DT 20.00 20.50 20.50 20.00 20.50 20.50 20.00 20.00 20.00
helicopter:NN   9.03   9.72 12.50   8.15 10.50 19.83 10.00 15.00 10.51
were:VBD 15.00 15.50 12.50 15.00 15.50 20.50 13.00   0.00   5.30
killed:VBN 15.00 15.50 12.50 13.15 15.50 19.03 15.00   5.30   0.00
a:DT 20.00 20.50 20.50 20.00 20.50 20.50 20.00 20.00 20.00
crash:NN   9.18   7.17 12.50   7.75 10.50 20.08 10.00 15.00   9.90
NO_WORD 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 -1.11 Alignment.score
 1.00  0.52 Alignment.isGood
-1.00  0.03 Alignment.isBad
 0.10  9.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "crash" modifying "killed" is dropped on aligned hypothesis word "killed"
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Iran"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 2.3118
Threshold: -1.1456

Inference ID: 704

Txt: In 1541 the Turks took the Buda and held it until 1686; the city changed very little during this time.

Hyp: The Turks held Buda between 1541 and 1686. (yes)

The
DT		 Turks
NNPS		 held
VBD		 Buda
NNP		 1541
CD		 1686
CD
1541:CD 20.50 20.50 20.50 20.50   0.00   5.00
the:DT   0.00 20.50 18.57 20.50 20.50 20.50
Turks:NNPS 20.50   0.00 15.50   9.39 20.50 20.50
took:VBD 18.57 12.17   0.00 15.50 20.50 20.50
the:DT   0.00 20.50 18.57 20.50 20.50 20.50
Buda:NNP 20.50   9.39 15.50   0.00 20.50 20.50
held:VBD 18.57 15.50   0.00 15.50 20.50 20.50
it:PRP 20.00 12.50 15.00 12.50 20.50 20.50
1686:CD 20.50 20.50 20.50 20.50   5.00   0.00
the:DT   0.00 20.50 18.57 20.50 20.50 20.50
city:NN 20.00   8.97 15.00 10.50 20.50 20.50
changed:VBD 20.00 15.50   4.76 15.50 20.50 20.50
very:RB 20.00 14.39 17.50 15.50 20.50 20.50
little:JJ 18.89 12.50 11.74 12.50 12.50 12.50
this:DT   5.71 17.17 20.00 20.50 20.50 20.50
time:NN 15.71   9.39 12.64 10.50 20.50 20.50
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 -1.00 Alignment.score
 1.00  0.55 Alignment.isGood
-1.00  0.03 Alignment.isBad
 0.10  6.00 Alignment.hypSpan
 0.10  0.83 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 01/01/1541
 1.00  1.00 Hypernym.posWiden : Widening a term (from "take" to "hold") preserves truth in a positive context
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Buda"
Hand-tuned score (dot product of above): 2.7039
Threshold: -1.1456

Inference ID: 1376

Txt: Hyperhidrosis is more common than you think. This condition affects 1 out of 25 people.

Hyp: Asians have an even higher rate of incidence---1 of every 5 suffers from hyperhidrosis. (don't know)

Asians
NNPS		 have
VBP		 an
DT		 even
RB		 higher
JJR		 rate
NN		 incidence
NN		 1
CD		 every
DT		 5
CD		 suffers
VBZ		 hyperhidrosis
NNS
Hyperhidrosis:NNS 10.50 15.50 20.50 15.50 12.50 10.50 10.50 20.50 20.50 20.50 15.50   0.00
is:VBZ 15.50   3.06 20.00 20.00 12.00 15.00 15.00 20.50 20.00 20.50   6.17 15.50
more:RBR 15.50 17.50 20.00 10.00 12.00 12.50 15.00 20.50 18.89 20.50 20.00 15.50
common:JJ 12.50 12.00 20.00 12.00 10.00 12.00 11.26 12.50 20.00 12.50 10.95 12.50
than:IN 18.50 17.50 16.67 17.50 20.00 20.00 20.00 20.50 20.00 20.50 20.00 20.50
you:PRP 12.50 15.00 20.00 20.00 15.00 12.00 12.00 20.50 20.00 20.50 15.00 12.50
think:VBP 12.77   4.52 20.00 18.89 11.09 14.97 14.45 20.50 20.00 20.33   7.17 15.50
This:DT 18.50 20.00 10.00 20.00 20.00 20.00 20.00 20.50 10.00 20.50 20.00 20.50
condition:NN   7.59 15.00 20.00 15.00 12.00   6.10   6.60 20.50 20.00 20.46 11.93 10.50
affects:VBZ 13.19   5.43 20.00 20.00 10.87 15.00 10.63 19.61 20.00 20.48   5.71 15.50
1:CD 20.50 20.50 20.50 20.50 12.50 20.50 20.29   0.00 20.50   5.00 20.50 20.50
out:RP 20.50 20.00 10.00 20.00 20.00 18.57 20.00 20.50 10.00 20.50 20.00 20.50
25:CD 20.50 20.50 20.50 20.50 12.50 17.82 20.31   5.00 20.50   5.00 20.21 20.50
people:NNS   7.88 15.00 20.00 15.00 12.00   6.44   8.64 20.50 19.09 20.50 15.00 10.50
NO_WORD 10.00 10.00   1.00   9.00   9.00 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 -6.48 Alignment.score
 1.00  0.01 Alignment.isGood
-1.00  0.88 Alignment.isBad
 0.10  3.00 Alignment.hypSpan
 0.10  0.42 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "higher" modifying "rate"
-4.00  0.27 NullPunisher.other : higher
-4.00  0.08 NullPunisher.other : even
-0.10  1.00 NullPunisher.functionWord : an
-3.00  1.00 NullPunisher.entity : Asians
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '5.0' vs '25.0'
Hand-tuned score (dot product of above): -18.5246
Threshold: -1.1456

Inference ID: 755

Txt: The Alameda Central ( Central Mall ) roughly marks the western edge of the old colonial city.

Hyp: The Alameda Central is west of the Zocalo. (don't know)

The
DT		 Alameda_Central
NNP		 is
VBZ		 west
NN		 the
DT		 Zocalo
NNP
The:DT   0.00 20.50 20.00 20.00   0.00 20.50
Alameda_Central:NNP 20.50   0.00 15.50   6.98 20.50 10.00
Central_Mall:NNP 20.50   0.50 15.50   6.98 20.50 10.50
roughly:RB 20.00 15.50 20.00 14.69 20.00 13.19
marks:VBZ 20.00 15.50   6.26 14.60 20.00 15.50
the:DT   0.00 20.50 20.00 20.00   0.00 20.50
western:JJ 20.00 12.50 12.00   5.17 20.00 12.50
edge:NN 18.57   7.53 15.00   5.66 18.57 10.50
the:DT   0.00 20.50 20.00 20.00   0.00 20.50
old:JJ 20.00 12.50 12.00 10.90 20.00 11.39
colonial:JJ 20.00 12.50 12.00 10.27 20.00 11.07
city:NN 20.00   5.25 15.00   5.13 20.00 10.50
NO_WORD   1.00 10.00   1.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -3.19 Alignment.score
 1.00  0.12 Alignment.isGood
-1.00  0.21 Alignment.isBad
 0.10  2.00 Alignment.hypSpan
 0.10  0.33 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Zocalo" modifying "west"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "colonial" modifying "city" is dropped on aligned hypothesis word "west"
-0.10  1.00 NullPunisher.functionWord : the
-3.00  1.00 NullPunisher.entity : Zocalo
-0.10  0.50 NullPunisher.functionWord : is
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Alameda_Central"
Hand-tuned score (dot product of above): -5.6963
Threshold: -1.1456

Inference ID: 1808

Txt: Vanunu converted to Christianity while in prison, and has been living in an anglican cathedral in Jerusalem since his release on April 21.

Hyp: A convert to Christianity, Vanunu has sequestered himself at a Jerusalem church since he was freed on April 21. (yes)

A
NNP		 convert
VBP		 Christianity
NNP		 Vanunu
NNP		 has
VBZ		 sequestered
VBN		 himself
PRP		 a
DT		 Jerusalem
NNP		 church
NN		 since
IN		 he
PRP		 was
VBD		 freed
VBN		 April
NNP		 21
CD
NO_WORD 10.50 10.00 10.00 10.00   1.00 10.00 10.00   1.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00
Vanunu:NNP 15.00 14.73 10.50   0.00 15.50 15.50   0.00 20.50 10.50 10.50 19.59   0.00 15.50 15.50   9.59 20.50
converted:VBD   9.58   0.00 15.00 15.50   2.50   7.00 15.50 20.00 14.39 14.33 20.00 15.50   6.62   8.57 15.50 19.51
Christianity:NNP   7.47 15.00   0.00 10.50 15.00 14.57 10.50 20.00   9.07   9.28 20.00 10.50 15.00 15.00 10.14 20.50
while:NN   9.09 15.00   9.64 10.50 15.00 15.00 10.50 20.00 10.13   9.09 18.00 10.50 15.00 15.00   6.91 20.50
prison:NN 15.00 15.00   8.89 10.50 15.00 12.35 10.50 20.00   9.75   8.93 19.09 10.50 15.00 11.46   7.77 19.25
has:VBZ 15.00   7.30 15.00 15.50   0.00   8.77 15.50 20.00 15.50 15.00 20.00 15.50   3.06   6.40 15.50 20.50
been:VBN 15.00   6.62 15.00 15.50   3.06   8.33 15.50 20.00 15.50 15.00 20.00 15.50   0.00   5.60 15.50 20.50
living:VBG 10.50   8.96 15.00 13.83   6.96   7.95 13.83 20.00 15.50 10.45 19.09 13.83   0.00   8.43 14.59 20.50
an:DT   9.77 20.00 20.00 20.50 18.00 20.00 20.50   0.50 20.50 20.00 17.93 20.50 18.00 20.00 20.50 20.50
anglican:NN   9.64 15.00   9.90 10.50 15.00 14.33 10.50 20.00   9.88   4.07 20.00 10.50 15.00 14.50   9.73 20.48
cathedral:NN 10.06 12.50   9.52 10.50 15.00 12.90 10.50 20.00   9.85   1.25 20.00 10.50 15.00 14.03   9.07 20.50
Jerusalem:NNP 10.50 15.50   9.07 10.50 15.50 12.50 10.50 20.50   0.00 10.23 20.50 10.50 15.50 15.50 10.13 20.50
his:PRP$ 10.50 14.73 10.50   0.00 15.50 15.50   0.00 20.50 10.50 10.50 19.59   0.00 15.50 15.50   9.59 20.50
release:NN   9.08 14.51   9.79 10.50 15.00 13.89 10.50 20.00   8.00   9.36 20.00 10.50 15.00 13.33   9.69 20.50
April:NNP   7.96 15.50 10.14   9.59 15.50 15.50   9.59 20.50 10.13   9.59 20.50   9.59 15.50 15.50   0.00 20.00
21:CD 20.50 20.14 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.50 20.00   0.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -3.67 Alignment.score
 1.00  0.08 Alignment.isGood
-1.00  0.30 Alignment.isBad
 0.10  6.00 Alignment.hypSpan
 0.10  0.44 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "anglican" modifying "cathedral" is dropped on aligned hypothesis word "church"
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 04/21/1000
 1.00  1.00 Hypernym.posWiden : Widening a term (from "cathedral" to "church") preserves truth in a positive context
-0.10  0.50 NullPunisher.functionWord : was
-4.00  0.21 NullPunisher.other : himself
-4.00  0.11 NullPunisher.other : since
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Jerusalem"
Hand-tuned score (dot product of above): -1.5759
Threshold: -1.1456

Inference ID: 837

Txt: The Clark County medical examiner's office said the man who was killed was 33 years old.

Hyp: The Clark County medical examiner's office put the dead man's age at 33. (yes)

The
DT		 Clark_County
JJ		 medical_examiner
POS		 office
NN		 put
VBD		 the
DT		 dead
JJ		 man
NN		 age
NN		 33
CD
The:DT   0.00 20.50 10.00 20.00 20.00   0.00 20.00 20.00 16.67 20.50
Clark_County:JJ 20.50   0.00 20.50 12.50 12.50 20.50 10.50 12.50 12.50 12.50
medical_examiner:POS 10.00 20.50   0.00 16.34 20.00 10.00 16.68 18.25 19.72 20.44
office:NN 20.00 12.50 16.34   0.00 14.76 20.00 11.36   7.61   8.55 19.97
said:VBD 20.00 12.50 19.19 14.16   4.23 20.00   9.50 13.57 13.57 20.50
the:DT   0.00 20.50 10.00 20.00 20.00   0.00 20.00 20.00 16.67 20.50
man:NN 20.00 12.50 18.25   7.61 13.93 20.00   6.36   0.00   7.38 19.73
who:WP 16.67 15.50 20.00 12.00 15.00 16.67 15.00 12.00 12.00 20.50
was:VBD 20.00 12.50 20.00 15.00   3.50 20.00 10.57 11.67 15.00 20.50
killed:VBN 20.00 12.50 17.51 14.44   6.45 20.00   5.10 12.10 11.67 20.50
was:VBD 20.00 12.50 20.00 15.00   3.50 20.00 10.57 11.67 15.00 20.50
33:CD 20.50 12.50 20.44 19.97 20.50 20.50 12.50 19.73 19.80   0.00
years:NNS 20.00 12.50 20.00   7.77 15.00 20.00   8.67   7.87   6.46 20.50
old:JJ 20.00 10.50 20.00 12.00 10.85 20.00   8.20   9.75   8.21 10.71
NO_WORD   1.00   9.00 10.00 10.00 10.00   1.00   9.00 10.00 10.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -2.38 Alignment.score
 1.00  0.24 Alignment.isGood
-1.00  0.11 Alignment.isBad
 0.10  10.00 Alignment.hypSpan
 0.10  0.70 Alignment.txtSpan
Hand-tuned score (dot product of above): -1.1817
Threshold: -1.1456

Inference ID: 2144

Txt: President Nicanor Duarte Frutos asked the private sector to help the state's overwhelmed public health system.

Hyp: President Nicanor Duarte called the tragedy "a moment of great anguish" for his country as he visited the scene. (don't know)

President
NNP		 Nicanor_Duarte
NNP		 called
VBD		 the
DT		 tragedy
NN		 a
DT		 moment
NN		 great
JJ		 anguish
NN		 his
PRP$		 country
NN		 as
IN		 he
PRP		 visited
VBD		 the
DT		 scene
NN
President:NNP   0.00 10.50 15.00 20.00   8.31 20.00   7.52 10.57   9.41 10.50   7.25 20.00 10.50 12.50 20.00   7.75
Nicanor_Duarte_Frutos:NNP 10.50   4.00 15.50 20.50 10.50 20.50 10.50 12.50 10.50   4.00 10.50 20.50   4.00 15.50 20.50 10.50
asked:VBD 15.00 15.50   5.45 20.00 13.33 20.00 12.98 12.00 14.15 15.50 15.00 18.57 15.50   6.67 20.00 13.00
the:DT 20.00 20.50 20.00   0.00 20.00 10.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50 20.00   0.00 20.00
private:JJ   9.50 12.02 10.37 20.00 10.57 20.00 11.23   6.67 11.05 12.02   9.15 20.00 12.02   9.14 20.00 12.00
sector:NN   8.34 10.50 15.00 20.00   8.89 20.00   8.26 11.09   8.54 10.50   7.25 20.00 10.50 14.23 20.00   7.27
to:TO 20.00 20.50 20.00   8.00 20.00 10.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50 20.00   8.00 20.00
help:VB 15.00 15.50   7.15 18.57 15.00 20.00 15.00 10.89 14.90 15.50 12.92 20.00 15.50   7.38 18.57 13.89
the:DT 20.00 20.50 20.00   0.00 20.00 10.00 20.00 20.00 20.00 20.50 20.00 20.00 20.50 20.00   0.00 20.00
state:NN   6.21 10.50 14.09 17.50   7.84 20.00   6.94 10.00   9.15 10.50   0.00 20.00 10.50 13.33 17.50   5.25
overwhelmed:CD 19.50 20.50 19.91 20.50 19.21 20.50 19.33 11.34 18.30 20.50 19.32 20.50 20.50 19.32 20.50 19.69
public:JJ 11.33 12.50 10.09 20.00 10.63 20.00 11.08   8.89 11.23 12.50 10.74 20.00 12.50 12.00 20.00 11.48
health:NN   8.13 10.50 13.33 20.00   8.04 20.00   8.04   9.27   7.73 10.50   7.80 20.00 10.50 14.23 20.00   8.76
system:NN   6.48 10.50 12.69 20.00   8.15 20.00   7.32 12.00   9.33 10.50   7.04 20.00 10.50 11.15 20.00   7.27
NO_WORD 10.00 10.00 10.00   1.00 10.00   1.00 10.00   9.00 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 -6.09 Alignment.score
 1.00  0.01 Alignment.isGood
-1.00  0.83 Alignment.isBad
 0.10  5.00 Alignment.hypSpan
 0.10  0.50 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "public" modifying "system" is dropped on aligned hypothesis word "scene"
-1.00  1.00 Hypernym.posNarrow : Narrowing a term (from "ask" to "call") does NOT preserve truth in a positive context
-0.10  1.00 NullPunisher.functionWord : his
-0.10  1.00 NullPunisher.functionWord : the
-4.00  0.28 NullPunisher.other : moment
-0.10  1.00 NullPunisher.functionWord : as
-0.10  1.00 NullPunisher.functionWord : a
-4.00  0.02 NullPunisher.other : he
Hand-tuned score (dot product of above): -8.4797
Threshold: -1.1456

Inference ID: 845

Txt: Israeli Prime Minister Ariel Sharon threatened to dismiss Cabinet ministers who don't support his plan to withdraw from the Gaza Strip.

Hyp: Israeli Prime Minister Ariel Sharon threatened to fire cabinet opponents of his Gaza withdrawal plan. (yes)

Israeli
NNP		 Prime_Minister
NNP		 Ariel_Sharon
NNP		 threatened
VBD		 to
TO		 fire
VB		 cabinet
NN		 opponents
NNS		 his
PRP$		 Gaza
NNP		 withdrawal
NN		 plan
NN
NO_WORD   0.00   8.90   9.97 13.74 20.50 14.59   9.61   8.82   9.97 10.00 10.00 10.00
Israeli:NNP   8.90   0.00   8.96 14.17 20.00 15.00   9.52   8.13   9.97   9.86   8.74   9.28
Prime_Minister:NNP   9.97   8.96   0.00 15.50 20.50 15.50 10.25 10.50   8.96   9.81   9.44   8.21
Ariel_Sharon:NNP 13.74 14.17 15.50   0.00 20.00   7.13 13.74 13.02   0.00 10.50   9.59 10.50
threatened:VBD 20.50 20.00 20.50 20.00   0.00 20.00 20.50 20.00 15.50 15.50 13.58 11.16
to:TO 14.07 14.52 15.50   7.95 20.00   0.00 15.04 13.82 20.50 20.50 20.00 20.00
dismiss:VB   8.70   3.87   8.78 14.01 20.00 15.00   4.88   7.67 15.50 15.50 14.43 14.71
Cabinet_ministers:NNS 12.50 12.00 12.50 15.00 18.00 15.00 12.50 12.00   8.78   9.73   8.61   7.36
who:WP 15.50 15.00 15.50   7.82 15.00   8.24 15.50 14.98 12.50 12.50 12.00 12.00
do:VBP 15.50 15.00 15.50 18.79 20.00 19.36 15.50 15.00 15.50 15.50 15.00 14.43
n't:RB 15.50 15.00 15.50   6.63 20.00   6.25 14.07   9.89 15.50 15.50 15.00 14.23
support:VB 12.50 12.00 12.50 15.00 20.00 13.57 12.50 12.00 15.50 15.50 13.05 12.26
his:PRP$   9.28   8.21 10.50 11.16 20.00 14.58   8.88   7.86 12.50 12.50 12.00 12.00
plan:NN 20.50 20.00 20.50 20.00   0.00 20.00 20.50 20.00 10.50   9.80   7.68   0.00
to:TO 14.83 15.00 14.50   7.00 20.00   8.32 15.50 14.60 20.50 20.50 20.00 20.00
withdraw:VB 20.50 20.00 20.50 20.00   8.00 18.57 20.50 20.00 14.50 15.50   1.00 12.46
the:DT   9.86   9.81   8.68 15.50 20.50 15.50   9.84   9.78 20.50 20.50 20.00 20.00
Gaza_Strip:NNP 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00   8.68   0.00 10.38   9.80

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -2.82 Alignment.score
 1.00  0.16 Alignment.isGood
-1.00  0.16 Alignment.isBad
 0.10  7.00 Alignment.hypSpan
 0.10  0.58 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "support" modifying "Cabinet_ministers" is dropped on aligned hypothesis word "cabinet"
 1.00  1.00 Hypernym.posWiden : Widening a term (from "dismiss" to "fire") preserves truth in a positive context
-0.10  1.00 NullPunisher.functionWord : his
-4.00  0.34 NullPunisher.other : opponents
 1.00  1.00 WorldKnowledge.match : Location match: "Gaza_Strip" entails "Gaza"
Hand-tuned score (dot product of above): -1.0016
Threshold: -1.1456

Inference ID: 747

Txt: The city was traditionally inhabited by mestizos ( people of mixed European and Indian descent ) and criollos ( Mexicans of European descent ), but steady immigration from the countryside has given it a more Indian character.

Hyp: People known as mestizos are of mixed European and Indian descent. (yes)

People
NNS		 known
VBN		 mestizos
NNS		 are
VBP		 mixed
JJ		 European
JJ		 Indian
JJ		 descent
NNS
NO_WORD 20.00 10.00 10.00 10.00   9.00   9.00   9.00 10.00
The:DT   5.88 20.00 20.00 16.67 20.00 20.50 20.50 20.00
city:NN 15.00 14.32 10.00 15.00   8.87 12.50 12.50   7.24
was:VBD 15.00   5.45 15.00   0.00 12.00 12.50 12.50 15.00
traditionally:RB 15.00 19.03 15.00 20.00 10.81 12.50 12.50 13.41
inhabited:VBN 10.00   6.34 14.41   1.25 10.57 12.50 10.50 10.59
mestizos:NNS   0.00 15.00   0.00 15.00 11.23 12.50 12.50   8.00
people:NNS 12.00 14.09 10.00 15.00 12.00 11.07 12.50   2.50
mixed:JJ 11.07 12.00 11.23 12.00   0.00 10.50 10.50 10.03
European:JJ 12.50 11.73 12.50 12.50 10.50   0.00   7.63 11.83
Indian:JJ   6.71   9.77 12.50 12.50 10.50   7.63   0.00 11.73
descent:NNS   8.57 14.00   8.00 15.00 10.03 11.83 11.73   0.00
criollos:NNS   8.94 15.00   7.50 15.00 12.00 11.25 12.50 10.00
Mexicans:NNPS 11.07 15.50   6.75 15.50 11.73 10.75   9.14   7.17
European:JJ   6.71 11.73 12.50 12.50 10.50   0.00   7.63 11.83
descent:NN 12.00 14.00   8.00 15.00 10.03 11.83 11.73   0.00
steady:JJ   8.65 12.00 10.57 12.00   6.55 10.50 10.50 10.81
immigration:NN 20.00 15.00   8.42 15.00 12.00 11.97 10.74   7.78
the:DT   8.77 20.00 20.00 16.67 20.00 20.50 20.50 20.00
countryside:NN 15.00 14.29 10.00 15.00 11.92 11.97 12.50   6.85
has:VBZ 15.00   1.00 15.00   3.06 12.00 12.50 12.50 15.00
given:VBN   5.88   6.31 15.00   5.21   8.00 11.73 11.59 13.33
it:PRP   5.88 14.32 10.00 15.00   8.87 12.50 12.50   7.24
a:DT 20.00 20.00 20.00 20.00 20.00 20.50 20.50 20.00
more:JJR 10.00 10.89 12.00   7.71   6.67 10.50 10.50 12.00
Indian:JJ 12.50   9.77 12.50 12.50 10.50   7.63   0.00 11.73
character:NN   6.35 12.79 10.00 15.00 11.45 11.91 12.50   7.07

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -1.38 Alignment.score
 1.00  0.46 Alignment.isGood
-1.00  0.04 Alignment.isBad
 0.10  8.00 Alignment.hypSpan
 0.10  0.75 Alignment.txtSpan
 1.00  1.00 Hypernym.posWiden : Widening a term (from "have" to "know") preserves truth in a positive context
Hand-tuned score (dot product of above): 0.9143
Threshold: -1.1456

Inference ID: 1401

Txt: Excluding restructuring costs and the associated tax impact, net income for the recent quarter would have been $4 million, Seagate said.

Hyp: Excluding these items, Seagate would have posted earnings of $462 million, or 93 cents a share. (don't know)

these
DT		 items
NNS		 Seagate
NNP		 would
MD		 have
VB		 posted
VBN		 earnings
NNS		 $
$		 462
CD		 million
CD		 93
CD		 cents
NNS		 a
DT		 share
NN
restructuring:VBG 20.00 13.31 15.50 20.00   7.41   7.61 10.87 18.52 20.50 17.35 19.98 11.96 20.00 12.09
costs:NNS 20.00   6.10 10.50 18.00 15.00 10.45   5.21 19.77 20.27 19.60 20.11   4.50 20.00   5.20
the:DT   5.00 20.00 20.50 10.00 18.57 20.00 20.00 10.50 20.50 20.50 20.50 20.50 10.00 17.50
associated:VBN 20.00 12.09 12.56 20.00   5.67   7.50 11.97 20.50 19.73 19.14 19.15 14.58 20.00 13.64
tax:NN 20.00   6.13 10.50 20.00 13.57 14.11   4.38 19.60 20.50 19.32 20.50   7.95 20.00   4.37
impact:NN 20.00   7.88   9.73 20.00 15.00 14.48   7.15 20.42 20.32 19.73 20.49   8.48 20.00   7.15
net_income:NN 20.00   5.05   9.91 20.00 15.00 10.95   0.00 20.50 20.49 19.91 17.64   7.16 20.00   2.34
the:DT   5.00 20.00 20.50 10.00 18.57 20.00 20.00 10.50 20.50 20.50 20.50 20.50 10.00 17.50
recent:JJ 19.09 11.09 10.19 20.00 12.00 10.54 10.51 20.50 11.67 12.39 10.84   7.50 20.00 12.00
quarter:NN 20.00   7.48   7.64 20.00 14.09   9.97   4.03 19.78 20.50 18.26 17.81   5.05 20.00   4.91
would:MD 10.00 20.00 20.50   0.00 18.69 17.27 20.00 10.50 20.50 20.50 20.50 20.50 10.00 20.00
have:VB 16.67 15.00 14.59 18.69   0.00   6.79 15.00 20.50 20.50 20.50 20.50 15.50 20.00   9.44
been:VBN 18.89 13.89 15.50 20.00   3.06   6.03 15.00 20.50 20.50 20.50 20.50 14.39 20.00 15.00
$:$ 10.50 18.79 20.50 10.50 20.50 19.82 20.50   0.00 17.12 16.63 18.62 16.56 10.50 16.98
4:CD 20.50 20.50 20.50 20.50 20.50 20.50 20.50 18.33   5.00   5.00   5.00 20.00 20.50 20.50
million:CD 20.50 17.83 20.50 20.50 20.50 18.14 18.62 16.63   5.00   0.00   5.00 16.52 20.50 17.38
Seagate:NNP 20.50 10.50   0.00 20.50 14.59 14.73   9.83 20.50 20.50 20.50 20.50   8.83 20.50   7.17
said:VBD 20.00 14.57 14.59 18.89   3.82   6.57 13.92 20.50 20.21 20.42 20.50 15.27 20.00 11.67
NO_WORD 10.00 10.00 10.00 10.00   1.00 10.00 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 -3.95 Alignment.score
 1.00  0.06 Alignment.isGood
-1.00  0.37 Alignment.isBad
 0.10  10.00 Alignment.hypSpan
 0.10  0.57 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "93" modifying "cents"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "recent" modifying "quarter" is dropped on aligned hypothesis word "cents"
 1.00  1.00 Factive.factivePassage : Hyp aligned with txt under factive 'say' verb: said
-0.10  1.00 NullPunisher.functionWord : a
-3.00  1.00 NullPunisher.entity : 93
-6.00  1.00 Numeric.mismatch : MONEY mismatch: '$4.62E8' vs '$4000000.0'
Hand-tuned score (dot product of above): -11.8019
Threshold: -1.1456

Inference ID: 1383

Txt: The crew of Apollo 11, Neil Armstrong, Buzz Aldrin and Michael Collins, and other early astronauts were named "Ambassadors of Exploration" in a ceremony at the Smithsonian National Air and Space Museum.

Hyp: Aldrin was one of the astronauts honored by NASA at the Smithsonian National Air and Space Museum. (yes)

Aldrin
NNP		 was
VBD		 one
CD		 the
DT		 astronauts
NNS		 honored
VBN		 NASA
NNP		 the
DT		 Smithsonian_National_Air
NNP		 Space_Museum
NNP
The:DT 20.50 20.00 17.17   0.00 20.00 20.00 20.50   0.00 20.50 20.50
crew:NN 10.50 15.00 19.07 18.57   4.93 14.09   7.46 18.57   9.58   9.82
Apollo:NNP   8.58 15.50 20.50 20.50 10.35 14.73   9.50 20.50   9.54   9.66
11:CD 20.50 20.50   5.00 20.50 20.34 20.50 20.50 20.50 20.50 20.50
Neil_Armstrong:NNP 10.00 15.50 20.50 20.50   1.00 15.50   9.91 20.50   8.33   9.68
Buzz_Aldrin:NNP   5.00 15.50 20.50 20.50 10.35 15.50   9.93 20.50   9.79   9.82
Michael_Collins:NNP 10.00 15.50 20.50 20.50   9.73 15.50   9.46 20.50   8.78   9.10
other:JJ 12.50 12.00 10.00 15.00 12.00 10.33 12.50 15.00 12.50 12.50
early:JJ 11.59 12.00 12.50 20.00 12.00   8.53 11.39 20.00 12.50 12.50
astronauts:NNS   9.25 15.00 20.50 20.00   0.00 14.41 10.02 20.00   8.47   9.75
were:VBD 15.50   0.00 19.07 18.57 15.00   5.32 15.50 18.57 15.50 15.50
named:VBN 15.50   5.16 20.50 20.00 15.00   6.67 12.17 20.00 15.50 15.50
Ambassadors:NNP 10.50 15.00 20.50 20.00   7.62 15.00 10.15 20.00   8.58   9.92
Exploration:NNP   8.74 15.00 20.50 20.00   8.57 15.00   9.94 20.00   9.37   9.64
a:DT 20.50 20.00 20.50 10.00 20.00 20.00 20.50 10.00 20.50 20.50
ceremony:NN 10.50 15.00 20.50 20.00   9.23   8.88   9.73 20.00   9.05   9.37
the:DT 20.50 20.00 17.17   0.00 20.00 20.00 20.50   0.00 20.50 20.50
Smithsonian_National_Air:NNP 10.25 15.50 20.50 20.50   8.47 15.50   8.97 20.50   0.00   8.55
Space_Museum:NNP 10.25 15.50 20.50 20.50   9.75 15.50   9.23 20.50   8.55   0.00
NO_WORD 10.00   1.00 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 -3.68 Alignment.score
 1.00  0.08 Alignment.isGood
-1.00  0.31 Alignment.isBad
 0.10  6.00 Alignment.hypSpan
 0.10  0.30 Alignment.txtSpan
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "Neil_Armstrong" modifying "crew" is dropped on aligned hypothesis word "NASA"
-0.10  1.00 NullPunisher.functionWord : the
-0.10  0.50 NullPunisher.functionWord : was
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '1.0' vs '11.0'
Hand-tuned score (dot product of above): -8.9263
Threshold: -1.1456

Inference ID: 863

Txt: The governor of Qazvin province Massoud Emami, his deputy, the province's head of police and three crew died when a helicopter taking them to inspect the earthquake zone crashed on Saturday.

Hyp: Massoud Emami, governor of Iran's northwestern province of Qazvin, and at least 9 others on abroad a helicopter were killed in a crash Saturday afternoon. (don't know)

Massoud_Emami
RB		 governor
NN		 Iran
NNP		 northwestern
NN		 province
NN		 Qazvin
NNP		 at
IN		 least
JJS		 9
CD		 others
NNS		 abroad
RB		 a
DT		 helicopter
NN		 were
VBD		 killed
VBN		 a
DT		 crash
NN		 Saturday
NNP		 afternoon
NN
NO_WORD 20.50 10.00 10.00 10.00 10.00 10.00 10.00   9.00 10.00 10.00   9.00   1.00 10.00   1.00 10.00   1.00 10.00 10.00 10.00
The:DT 15.50 20.00 20.50 20.00 20.00 20.50 20.00 20.00 20.50 16.67 20.00 10.00 20.00 18.57 20.00 10.00 20.00 20.50 20.50
governor:NN 15.50   0.00   9.68   8.21   7.50 10.50 20.00 12.00 20.50   8.57 15.00 20.00   9.03 15.00 15.00 20.00   9.18   9.25   6.38
Qazvin:NNP 15.50 10.50 10.00 10.50   7.64   0.00 20.50 12.50 20.50 10.50 15.50 20.50 10.50 15.50 15.50 20.50 10.50 10.50 10.50
province:NN   0.00   7.50   6.06   8.29   0.00   7.64 20.00 12.00 19.71 10.00 15.00 20.00   8.15 15.00 13.15 20.00   7.75   8.69   8.57
Massoud_Emami:NNP   0.00 10.50 10.50 10.50 10.50 10.50 20.50 12.50 20.50 10.50 15.50 20.50 10.50 15.50 15.50 20.50 10.50 10.02 10.50
his:PRP$   0.00 10.50 10.50 10.50 10.50 10.50 20.50 12.50 20.50 10.50 15.50 20.50 10.50 15.50 15.50 20.50 10.50 10.02 10.50
deputy:NN 15.50   7.06   9.68 10.00   7.77 10.50 20.00   9.27 20.50 10.00 14.77 20.00   8.04 15.00 13.75 20.00   9.14   9.07   9.45
the:DT 20.50 20.00 20.50 20.00 20.00 20.50 13.50 20.00 20.50 16.67 20.00 10.00 20.00 18.57 20.00 10.00 20.00 20.50 20.50
province:NN 15.50   7.50   6.06   8.29   0.00   7.64 20.00 12.00 19.71 10.00 15.00 20.00   8.15 15.00 13.15 20.00   7.75   8.69   8.57
head:NN 15.50   8.92   8.00   8.56   7.29 10.50 20.00   8.67 20.50   8.00 13.00 20.00   8.73 12.50 14.74 20.00   8.83   9.62   9.54
police:NN 15.50   8.96   9.56 10.00   4.29 10.50 20.00 12.00 20.50   9.02 15.00 20.00   5.33 15.00   9.07 20.00   6.22   9.31   9.22
three:CD 20.50 20.50 19.39 19.91 19.57 20.50 20.50 12.50   5.00 17.77 19.59 20.50 20.50 17.17 19.43 20.50 20.18 19.73 20.50
crew:NN 15.50   9.58   8.00 10.00   8.53 10.50 20.00 12.00 19.27   9.18 15.00 20.00   5.57 12.50 12.57 20.00   6.23   9.99   8.41
died:VBD 20.50 14.96 15.50 14.56 13.52 15.50 20.00 12.00 20.50 15.00 19.16 20.00 13.91   4.72   3.80 20.00 11.03 15.50 14.92
when:WRB 20.50 20.00 18.00 20.00 20.00 20.50 20.00 20.00 20.50 18.00 20.00 10.00 20.00 17.50 20.00 10.00 20.00 20.50 20.50
a:DT 20.50 20.00 20.50 20.00 20.00 20.50 16.67 20.00 20.50 20.00 20.00   0.00 20.00 20.00 20.00   0.00 20.00 20.50 20.50
helicopter:NN 15.50   9.03   9.72   8.18   8.15 10.50 20.00 12.00 20.44 10.00 15.00 20.00   0.00 15.00 10.51 20.00   5.17 10.07   9.18
taking:VBG 20.50 15.00 15.50 14.98 13.57 12.17 20.00 12.00 19.62 15.00 19.50 20.00 13.82   5.03   7.49 20.00 12.93 15.50 15.42
them:PRP 15.50   9.58   8.00 10.00   8.53 10.50 20.00 12.00 19.27   9.18 15.00 20.00   5.57 12.50 12.57 20.00   6.23   9.99   8.41
to:TO 20.50 20.00 20.50 20.00 20.00 20.50 18.44 20.00 20.50 20.00 20.00 10.00 20.00 20.00 20.00 10.00 20.00 20.50 20.50
inspect:VB 20.50 13.81 15.50 13.45 13.89 15.50 20.00 12.00 20.50 14.23 20.00 20.00 10.92   6.53   8.45 20.00 11.02 15.50 15.50
the:DT 20.50 20.00 20.50 20.00 20.00 20.50 13.50 20.00 20.50 16.67 20.00 10.00 20.00 18.57 20.00 10.00 20.00 20.50 20.50
earthquake:NN 15.07   8.71   9.31   7.27   6.93 10.50 20.00 12.00 20.01 10.00 14.13 20.00   8.97 15.00 12.82 20.00   7.79   8.28   8.92
zone:NN 15.50   9.15   8.11   8.01   6.40 10.50 20.00 12.00 19.85 10.00 13.87 20.00   6.86 12.50 14.83 20.00   8.83   9.87   9.64
crashed:VBD 20.50 15.00 14.59 14.47 14.30 14.73 20.00 10.33 20.50 14.23 19.23 20.00 10.58   7.52   4.28 20.00   0.00 14.83 14.23
Saturday:NNP 15.02   9.25   9.98 10.50   8.69 10.50 20.50 12.50 20.50 10.50 12.64 20.50 10.07 15.50 15.50 20.50   9.54   0.00   7.21

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -4.85 Alignment.score
 1.00  0.03 Alignment.isGood
-1.00  0.59 Alignment.isBad
 0.10  6.00 Alignment.hypSpan
 0.10  0.21 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "afternoon" modifying "killed"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "when" modifying "crashed" is dropped on aligned hypothesis word "crash"
 1.00  1.00 Date.matchDatesByGraph : hyp/txt matching, by graph: Saturday and children
-4.00  0.18 NullPunisher.other : others
-4.00  0.40 NullPunisher.other : abroad
-0.10  0.50 NullPunisher.functionWord : were
-3.00  1.00 NullPunisher.entity : afternoon
-0.10  1.00 NullPunisher.functionWord : a
-4.00  0.16 NullPunisher.other : least
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '>=9.0' vs '3.0'
-6.00  1.00 Numeric.mismatch : date A != 01/01/1000
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Qazvin"
Hand-tuned score (dot product of above): -21.4395
Threshold: -1.1456

Inference ID: 858

Txt: Each hour spent in a car was associated with a 6 percent increase in the likelihood of obesity and each half-mile walked per day reduced those odds by nearly 5 percent, the researchers found.

Hyp: The more driving you do means you're going to weigh more -- the more walking means you're going to weigh less. (yes)

The
DT		 more
JJR		 driving
NN		 you
PRP		 do
VBP		 means
VBZ		 you
PRP		 're
VBP		 going
VBG		 to
TO		 weigh
VB		 more
JJR		 the
DT		 more
JJR		 walking
NN		 means
VBZ		 you
PRP		 're
VBP		 going
VBG		 to
TO		 weigh
VB		 less
JJR
Each:DT 10.00 20.00 20.00 20.00 20.00 16.67 20.00 20.00 20.00 10.00 16.67 20.00 10.00 20.00 20.00 16.67 20.00 20.00 20.00 10.00 16.67 20.00
hour:NN 20.00   9.50   7.51   7.71 15.00 15.00   7.71 14.34 13.89 20.00 15.00   9.50 20.00   9.50   5.55 15.00   7.71 14.34 13.89 20.00 15.00 12.00
spent:VBN 20.00 12.00 15.00 15.00   6.04   4.85 15.00   9.41   4.99 20.00   8.33 12.00 20.00 12.00 14.03   4.85 15.00   9.41   4.99 20.00   8.33 10.89
a:DT 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00
car:NN 20.00 10.57   6.75 12.00 14.50 15.00 12.00 15.00 14.88 20.00 14.88 10.57 20.00 10.57   7.05 15.00 12.00 15.00 14.88 20.00 14.88 12.00
was:VBD 20.00 12.00 15.00 15.00   4.07   2.50 15.00   0.00   2.75 20.00   7.09 12.00 20.00 12.00 15.00   2.50 15.00   0.00   2.75 20.00   7.09 10.57
associated:VBN 20.00 12.00 14.66 15.00   6.42   5.30 15.00 10.00   5.43 20.00   8.49 12.00 20.00 12.00 15.00   5.30 15.00 10.00   5.43 20.00   8.49 12.00
a:DT 10.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 10.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00
6:CD 20.50 12.50 17.90 20.50 20.50 20.23 20.50 20.50 20.50 20.50 20.43 12.50 20.50 12.50 18.70 20.23 20.50 20.50 20.50 20.50 20.43 12.50
percent:NN 20.50 11.59   7.50 12.50 14.79 13.83 12.50 15.03 15.22 20.50 15.14 11.59 20.50 11.59   9.07 13.83 12.50 15.03 15.22 20.50 15.14 12.50
increase:NN 20.00 12.00   8.38 12.00 14.98 12.94 12.00 15.00 15.00 20.00 14.48 12.00 20.00 12.00   8.05 12.94 12.00 15.00 15.00 20.00 14.48 12.00
the:DT   0.00 18.57 20.00 20.00 20.00 20.00 20.00 16.67 20.00   8.00 20.00 18.57   0.00 18.57 20.00 20.00 20.00 16.67 20.00   8.00 20.00 20.00
likelihood:NN 20.00 12.00   9.24 12.00 15.00 11.41 12.00 15.00 15.00 20.00   9.29 12.00 20.00 12.00   9.04 11.41 12.00 15.00 15.00 20.00   9.29 12.00
obesity:NN 20.00 12.00   8.57 12.00 14.85 15.00 12.00 14.56 14.64 20.00 13.27 12.00 20.00 12.00   8.57 15.00 12.00 14.56 14.64 20.00 13.27 11.09
each:DT 10.00 20.00 20.00 20.00 20.00 16.67 20.00 20.00 20.00 10.00 16.67 20.00 10.00 20.00 20.00 16.67 20.00 20.00 20.00 10.00 16.67 20.00
half-mile:JJ 20.00 10.00 10.87 15.00 12.00 12.00 15.00 11.03 10.42 20.00 12.00 10.00 20.00 10.00   7.52 12.00 15.00 11.03 10.42 20.00 12.00 10.00
walked:NN 20.00 12.00   8.28 12.00 15.00 15.00 12.00 15.00 14.69 20.00 14.09 12.00 20.00 12.00   1.00 15.00 12.00 15.00 14.69 20.00 14.09 12.00
day:NN 20.50 12.50   8.70 12.50 13.50 15.50 12.50 14.75 13.92 20.50 14.01 12.50 20.50 12.50   7.00 15.50 12.50 14.75 13.92 20.50 14.01 12.50
reduced:VBD 20.00 12.00 14.14 15.00   6.58   5.49 15.00 10.00   5.63 20.00   8.26 12.00 20.00 12.00 15.00   5.49 15.00 10.00   5.63 20.00   8.26 12.00
those:DT   5.00 16.67 20.00 20.00 20.00 20.00 20.00 20.00 20.00   8.57 20.00 16.67   5.00 16.67 20.00 20.00 20.00 20.00 20.00   8.57 20.00 18.89
odds:NNS 20.00 12.00   9.25 12.00 14.41 13.83 12.00 13.26 12.98 20.00 12.61 12.00 20.00 12.00   9.12 13.83 12.00 13.26 12.98 20.00 12.61   9.50
nearly:RB 20.00 12.00 13.74 15.00 20.00 17.27 15.00 20.00 20.00 20.00 19.09 12.00 20.00 12.00 14.43 17.27 15.00 20.00 20.00 20.00 19.09 12.00
5:CD 20.50 12.50 20.50 20.50 19.86 20.38 20.50 19.81 19.73 20.50 20.50 12.50 20.50 12.50 19.02 20.38 20.50 19.81 19.73 20.50 20.50 12.50
percent:NN 20.50 11.59   7.50 12.50 14.79 13.83 12.50 15.03 15.22 20.50 15.14 11.59 20.50 11.59   9.07 13.83 12.50 15.03 15.22 20.50 15.14 12.50
the:DT   0.00 18.57 20.00 20.00 20.00 20.00 20.00 16.67 20.00   8.00 20.00 18.57   0.00 18.57 20.00 20.00 20.00 16.67 20.00   8.00 20.00 20.00
researchers:NNS 20.00 12.00   9.10 12.00 14.80 15.00 12.00 15.00 15.00 20.00 13.27 12.00 20.00 12.00   8.87 15.00 12.00 15.00 15.00 20.00 13.27 10.72
found:VBD 20.00 10.89 13.59 12.50   7.09   6.09 12.50 10.00   6.00 20.00   8.88 10.89 20.00 10.89 12.01   6.09 12.50 10.00   6.00 20.00   8.88 12.00
NO_WORD   1.00   9.00 10.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00   9.00   1.00   9.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00   9.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -6.95 Alignment.score
 1.00  0.00 Alignment.isGood
-1.00  0.92 Alignment.isBad
 0.10  4.00 Alignment.hypSpan
 0.10  0.14 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "more" modifying "walking"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "percent" modifying "reduced" is dropped on aligned hypothesis word "means"
 1.00  1.00 Hypernym.posWiden : Widening a term (from "be" to "mean") preserves truth in a positive context
-0.10  0.50 NullPunisher.functionWord : 're
-0.10  1.00 NullPunisher.functionWord : The
-4.00  0.07 NullPunisher.other : you
-4.00  0.06 NullPunisher.other : more
-4.00  0.56 NullPunisher.other : weigh
-4.00  0.22 NullPunisher.other : less
-4.00  0.07 NullPunisher.other : you
-0.10  1.00 NullPunisher.functionWord : the
-4.00  0.56 NullPunisher.other : weigh
-0.10  0.50 NullPunisher.functionWord : 're
-4.00  0.06 NullPunisher.other : more
-4.00  0.06 NullPunisher.other : more
 1.00  1.00 Quant.equivalent : Replacing the quantifier "a" by an equivalent quantifier "the" preserves truth.
Hand-tuned score (dot product of above): -12.8796
Threshold: -1.1456

Inference ID: 1363

Txt: A Filipino truck driver held hostage in Iraq for two weeks has been freed, a day after Manila withdrew its troops in response to demands from kidnappers who had threatened to behead him.

Hyp: Egyptian driver Mohammed al-Gharabawi was freed after the Saudi firm he worked for met kidnappers' demands by promising to stop doing work in Iraq. (don't know)

Egyptian
JJ		 driver
NN		 Mohammed_al-Gharabawi
NN		 was
VBD		 freed
VBN		 the
DT		 Saudi
JJ		 firm
NN		 he
PRP		 worked
VBD		 for
IN		 met
VBD		 kidnappers
NNS		 demands
NNS		 promising
VBG		 to
TO		 stop
VB		 doing_work
VBG		 Iraq
NNP
NO_WORD 20.50 10.00 10.00   1.00 10.00   1.00   9.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00 10.00
A:DT 10.75 20.00 20.50 20.00 20.00 10.00 20.50 20.00 20.50 20.00 20.00 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.50
Filipino:NNP 12.50   9.83   9.13 15.50 15.50 20.50 12.00   9.08   9.13 15.50 20.50 15.50   9.02   9.58 12.56 20.50 15.50 15.50   9.79
truck_driver:NN 12.50   0.00   9.97 15.00 14.12 20.00 12.50   8.89   9.97 15.00 20.00 15.00   7.97   9.31 14.44 20.00 12.22 15.00   9.93
held:VBD 11.83 15.00 15.50   4.49   6.67 18.57 11.39 14.23 15.50   7.05 20.00   7.09 15.00 14.09   6.86 20.00   7.45   6.53 15.50
hostage:NN 12.50   7.62   9.18 15.00 10.51 20.00 12.50   8.82   9.18 14.23 20.00 12.31   4.87   7.10 13.75 20.00 12.99 15.00   9.81
Iraq:NNP 12.50   9.88   9.88 14.07 14.39 20.50 12.50   8.00   9.88 15.50 20.50 15.50   9.83   9.88 15.50 20.50 15.50 15.50   0.00
two_weeks:NNS 12.50 10.00 10.50 15.00 13.28 20.00 12.50 10.00 10.50 13.00 20.00 12.42   7.70 10.00 13.89 20.00 15.00 13.42 10.50
has:VBZ 12.50 15.00 15.50   3.06   6.40 20.00 12.50 15.00 15.50   6.04 20.00   6.10 15.00 15.00   5.82 20.00   6.52   5.43 14.07
been:VBN 12.50 15.00 15.50   0.00   5.60 18.57 12.50 15.00 15.50   5.19 20.00   5.25 15.00 14.09   4.94 20.00   5.73   4.51 15.50
freed:VBN 20.50 12.27 15.50   5.60   0.00 20.00 12.50 13.89 15.50   5.45 20.00   7.83 11.16 12.97   7.64 20.00   8.13   7.36 14.39
a:DT 12.50 20.00 20.50 20.00 20.00 10.00 20.50 20.00 20.50 20.00 18.59 20.00 20.00 20.00 20.00 10.00 20.00 20.00 20.50
day:NN 20.50   9.09   9.98 12.17 14.04 20.50 12.50   7.05   9.98 15.17 20.50 13.72   9.66   7.93 15.50 20.50 13.74 14.43   9.07
after:IN 12.50 17.27 20.50 20.00 18.00 17.88 20.50 18.89 20.50 19.09   6.82 20.00 19.33 20.00 20.00 17.32 18.89 20.00 20.50
Manila:NNP 12.50   9.84   9.95 15.50 15.50 20.50 11.59   8.99   9.95 15.50 20.50 15.50   9.91   8.19 14.83 20.50 15.50 15.50   9.41
withdrew:VBD 12.50 15.00 15.50   6.30   6.63 19.09 12.50 14.28 15.50   8.25 20.00   7.87 13.45 13.19   7.68 20.00   1.25   7.87 15.50
its:PRP$ 12.50   9.84   9.95 15.50 15.50 20.50 11.59   8.99   9.95 15.50 20.50 15.50   9.91   8.19 14.83 20.50 15.50 15.50   9.41
troops:NNS 12.50   8.33 10.37 15.00 12.91 20.00 12.50   6.51 10.37 15.00 20.00 14.44   8.30   8.08 13.00 20.00 11.61 14.92 10.22
response:NN 11.25   8.96   9.88 15.00 14.95 20.00 12.50   7.80   9.88 14.60 20.00 12.37   8.53   7.83 13.18 20.00 11.92 14.85   9.83
to:TO 20.50 20.00 20.50 20.00 20.00   8.00 20.50 20.00 20.50 20.00 17.43 20.00 20.00 20.00 20.00   0.00 16.67 20.00 20.50
demands:NNS 12.50   8.89 10.13 15.00 12.97 20.00 10.83   7.06 10.13 14.23 20.00 13.75   7.73   0.00 12.70 20.00 14.27 13.48   9.88
kidnappers:NNS 12.50   7.97   9.21 15.00 11.16 20.00 12.50   8.97   9.21 15.00 20.00 12.56   0.00   7.73 14.91 20.00 14.44 13.00   9.83
who:WP 15.50 12.00 12.50 11.67 15.00 16.67 15.50 12.00 12.50 15.00 20.00 15.00 12.00 12.00 15.00 18.00 13.57 15.00 12.50
had:VBD 12.50 15.00 15.50   3.06   6.40 20.00 10.00 15.00 15.50   6.04 20.00   6.10 15.00 15.00   5.82 20.00   6.52   5.43 14.07
threatened:VBN 12.50 14.22 15.50   6.57   6.30 20.00 12.50 15.00 15.50   8.42 20.00   7.51 12.40 10.89   7.78 20.00   6.30   8.06 15.50
to:TO 20.50 20.00 20.50 20.00 20.00   8.00 20.50 20.00 20.50 20.00 17.43 20.00 20.00 20.00 20.00   0.00 16.67 20.00 20.50
behead:VB 12.50 15.00 15.50   8.56   7.27 18.89 12.50 15.00 15.50   8.33 20.00   9.58 15.00 12.69   9.50 20.00   9.68   9.39 15.50
him:PRP 15.50 12.00 12.50 15.00 15.00 20.00 15.50   7.71 12.50 15.00 20.00 15.00 12.00 12.00 15.00 20.00 15.00 15.00 12.50

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -5.37 Alignment.score
 1.00  0.02 Alignment.isGood
-1.00  0.71 Alignment.isBad
 0.10  10.00 Alignment.hypSpan
 0.10  0.21 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Saudi" modifying "firm"
 0.50  1.00 Adjunct.dropPosCxt : It is okay that text word "two_weeks" modifying "Iraq" is dropped on aligned hypothesis word "Iraq"
 1.00  1.00 Factive.inPositiveEmbedding : embedded positive text
 1.00  1.00 Hypernym.posWiden : Widening a term (from "truck_driver" to "driver") preserves truth in a positive context
 1.00  1.00 Hypernym.posWiden : Widening a term (from "withdraw" to "stop") preserves truth in a positive context
-3.00  1.00 NullPunisher.entity : Saudi
-4.00  0.02 NullPunisher.other : he
-0.10  0.50 NullPunisher.functionWord : was
-0.10  1.00 NullPunisher.functionWord : the
-3.00  1.00 NullPunisher.entity : Egyptian
 1.00  1.00 WorldKnowledge.match : Locations match: both are talking about "Iraq"
Hand-tuned score (dot product of above): -7.7892
Threshold: -1.1456

Inference ID: 861

Txt: Customers are starting to feel more comfortable with open source software, and they are even more comfortable if a big company like H-P stands behind it, said Bob Bickel, vice president for corporate strategy and development at JBoss.

Hyp: Bob Bickel, vice president of strategy and corporate development at JBoss, said commercial use remains somewhat constrained because a CIO doesn't know whom they can turn to for support. (don't know)

Bob_Bickel
NNP		 vice_president
NN		 strategy
NN		 corporate
JJ		 development
NN		 JBoss
NNP		 said
VBD		 commercial
JJ		 use
NN		 remains
VBZ		 somewhat
RB		 constrained
VBN		 because
IN		 a
DT		 CIO
NN		 does
VBZ		 n't
RB		 know
VB		 whom
WP		 they
PRP		 can
MD		 turn_to
VB		 support
NN
NO_WORD   9.23 10.00 10.00   9.00 10.00 10.00 10.00   9.00 10.00   1.00   9.00 10.00 10.00   1.00 10.00   1.00   9.00 10.00 10.00 10.00 10.00 10.00 10.00
Customers:NNS 15.50   8.25   7.67   9.78   7.99 10.00 15.00 10.42   7.51 15.00 13.24 13.00 20.00 20.00 10.50 14.23 15.00 15.00 12.00   0.00 20.00 15.00   7.50
are:VBP 15.50 15.00 15.00 12.00 15.00 15.00   2.72 12.00 11.67   0.81 20.00   6.86 20.00 20.00 15.50   4.07 20.00   5.45 15.00 15.00 15.08   4.83 15.00
starting:VBG 20.50 14.96 11.25 11.84 15.00 15.00   5.15 10.89 15.00   5.06 18.75   7.37 20.00 20.00 15.50   6.20 20.00   7.24 15.00 14.41 16.97   6.78 14.33
to:TO 15.50 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 10.00 18.50 17.95 20.00 20.00 20.00 20.00 10.00 20.00 20.00
feel:VB 15.50 15.00 15.00 11.98 15.00 15.00   5.51 12.00 13.57   5.41 19.13   8.45 20.00 20.00 15.50   6.50 16.74   5.21 15.00 15.00 17.23   7.06 15.00
more:RBR 11.07 15.00 15.00 11.23 15.00 13.89 20.00 12.00 13.57 20.00 10.00 20.00 20.00 20.00 15.50 17.50 10.00 20.00 15.00 15.00 20.00 20.00 14.09
comfortable:JJ 12.50 11.60 10.63   5.00 12.00 12.00   9.53   5.71