Txt/Hyp term similarities

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

Resource summary:
Acronym: AcronymLexicalResource
BasicWN: null
Country: CountryLexicalResource
Coref: CorefLexicalResource
Cyc: null
DekangLin: null
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: 01

Txt: Horses like apples.

Hyp: Horses like apples. (yes)

Horses
NNS		 like
VBP		 apples
NNS
Horses:NNS   0.00 15.00   6.67
like:VBP 15.00   0.00 13.23
apples:NNS   6.67 13.23   0.00
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.00 Alignment.score
 1.00  0.77 Alignment.isGood
-1.00  0.01 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): 1.1575
Threshold: -2.0000

Inference ID: 02

Txt: The boy saw a bird.

Hyp: The boy saw a comet. (don't know)

The
DT		 boy
NN		 saw
VBD		 a
DT		 comet
NN
The:DT   0.00 20.00 20.00 10.00 20.00
boy:NN 20.00   0.00 11.11 20.00   8.84
saw:VBD 20.00 11.11   0.00 20.00 14.99
a:DT 10.00 20.00 20.00   0.00 20.00
bird:NN 20.00   7.04 14.24 20.00   4.89
NO_WORD   1.00 10.00 10.00   1.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.98 Alignment.score
 1.00  0.56 Alignment.isGood
-1.00  0.03 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 1.00  1.00 Quant.contract : [a,a]
Hand-tuned score (dot product of above): 1.1485
Threshold: -2.0000

Inference ID: 03

Txt: George bought guns.

Hyp: George bought weapons. (yes)

George
NNP		 bought
VBD		 weapons
NNS
George:NNP   0.00 13.83   8.47
bought:VBD 13.83   0.00 15.00
guns:NNS   9.05 15.00   1.25
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.42 Alignment.score
 1.00  0.69 Alignment.isGood
-1.00  0.02 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 1.00  1.00 Hypernym.posWiden : widening in positive context: gun -> weapon
Hand-tuned score (dot product of above): 1.6532
Threshold: -2.0000

Inference ID: 04

Txt: Mars needs women.

Hyp: Mars doesn't need women. (don't know)

Mars
NNP		 does
VBZ		 n't
RB		 need
VB		 women
NNS
Mars:NNP   0.00 13.00 15.50 15.50   9.51
needs:VBZ 14.39   4.29 20.00   0.00 13.09
women:NNS   9.51 11.67 14.92 13.17   0.00
NO_WORD 10.00   1.00   9.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -2.00 Alignment.score
 1.00  0.31 Alignment.isGood
-1.00  0.08 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  2.00 Alignment.hypSpan
 0.10  0.60 Alignment.txtSpan
-6.00  1.00 Adjunct.diffPol : hyp and txt have different polarity
 0.00  1.00 NegPolarity.hypNegWord : "need": has child with relation "neg"
 0.00  1.00 NegPolarity.hypNegRoot : "need": has child with relation "neg"
-1.00  1.00 NullPunisher.other : n't
-0.05  1.00 NullPunisher.aux : does
Hand-tuned score (dot product of above): -8.7558
Threshold: -2.0000

Inference ID: 05

Txt: Honda manufactures cars.

Hyp: Honda makes cars. (yes)

Honda
NNP		 makes
VBZ		 cars
NNS
Honda:NNP   0.00 15.50 10.50
manufactures:VBZ 15.50   1.25 12.36
cars:NNS 10.50 11.67   0.00
NO_WORD 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.42 Alignment.score
 1.00  0.69 Alignment.isGood
-1.00  0.02 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): 0.6532
Threshold: -2.0000

Inference ID: 06

Txt: Bush was an excellent student.

Hyp: Bush was a terrible student. (don't know)

Bush
NNP		 was
VBD		 a
DT		 terrible
JJ		 student
NN
Bush:NNP   0.00 14.07 20.50 12.50   7.72
was:VBD 14.07   0.00 20.00 12.00 15.00
an:DT 20.50 18.00   0.50 20.00 20.00
excellent:JJ 12.50 12.00 20.00   8.24   9.50
student:NN   7.72 15.00 20.00 12.00   0.00
NO_WORD 10.00   1.00   1.00   9.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -1.75 Alignment.score
 1.00  0.37 Alignment.isGood
-1.00  0.06 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 1.00  1.00 Quant.contract : [a,a]
Hand-tuned score (dot product of above): 0.1596
Threshold: -2.0000

Inference ID: 07

Txt: The United States belongs to the United Nations.

Hyp: The U.S. belongs to the UN. (yes)

The
DT		 U.S.
NNP		 belongs
VBZ		 the
DT		 UN
NNP
The:DT   0.00 20.50 20.00   0.00 20.50
United_States:NNPS 20.50   0.00 15.50 20.50   9.25
belongs:VBZ 20.00 15.50   0.00 20.00 15.50
the:DT   0.00 20.50 20.00   0.00 20.50
United_Nations:NNPS 20.50   7.78 15.50 20.50   0.00
NO_WORD   1.00 10.00 10.00   1.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00  0.00 Alignment.score
 1.00  0.77 Alignment.isGood
-1.00  0.01 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
Hand-tuned score (dot product of above): 1.3575
Threshold: -2.0000

Inference ID: 08

Txt: Two senators objected.

Hyp: 14 senators objected. (don't know)

14
CD		 senators
NNS		 objected
VBD
Two:CD   5.00 20.50 20.50
senators:NNS 20.42   0.00 12.58
objected:VBD 20.32 12.58   0.00
NO_WORD 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -1.67 Alignment.score
 1.00  0.39 Alignment.isGood
-1.00  0.06 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
-6.00  1.00 Numeric.mismatch : NUMBER mismatch: '14.0' vs '2.0'
Hand-tuned score (dot product of above): -6.9368
Threshold: -2.0000

Inference ID: 09

Txt: The victim was a homeless immigrant.

Hyp: The victim was an immigrant. (yes)

The
DT		 victim
NN		 was
VBD		 an
DT		 immigrant
JJ
The:DT   0.00 20.00 20.00 10.00 20.00
victim:NN 20.00   0.00 15.00 20.00 10.40
was:VBD 20.00 15.00   0.00 18.00 12.00
a:DT 10.00 20.00 20.00   0.50 20.00
homeless:JJ 20.00   8.96 12.00 20.00   6.73
immigrant:NN 20.00   8.28 15.00 20.00   0.00
NO_WORD   1.00 10.00   1.00   1.00   9.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.10 Alignment.score
 1.00  0.75 Alignment.isGood
-1.00  0.01 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 "homeless" of "immigrant" dropped on aligned hyp word "immigrant"
 1.00  1.00 Quant.contract : [a,a]
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.2381
Threshold: -2.0000

Inference ID: 10

Txt: The deal included power plants.

Hyp: The deal included nuclear power plants. (don't know)

The
DT		 deal
NN		 included
VBD		 nuclear_power
NNS		 plants
NNS
The:DT   0.00 20.00 20.00 20.00 20.00
deal:NN 20.00   0.00 15.00   9.76   7.94
included:VBD 20.00 15.00   0.00 14.52 15.00
power_plants:NNS 20.00   9.17 15.00   6.30   0.00
NO_WORD   1.00 10.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -2.00 Alignment.score
 1.00  0.31 Alignment.isGood
-1.00  0.08 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : the hypothesis added the modifier word "nuclear_power"
-1.00  1.00 NullPunisher.other : nuclear_power
Hand-tuned score (dot product of above): -3.4858
Threshold: -2.0000

Inference ID: 11

Txt: The Taj Mahal is in India.

Hyp: The Taj Mahal is in Asia. (yes)

The
DT		 Taj_Mahal
NNP		 is
VBZ		 Asia
NNP
The:DT   0.00 20.50 20.00 20.50
Taj_Mahal:NNP 20.50   0.00 15.50 10.00
is:VBZ 20.00 15.50   0.00 15.50
India:NNP 20.50 10.00 15.50   6.67
NO_WORD   1.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -1.67 Alignment.score
 1.00  0.39 Alignment.isGood
-1.00  0.06 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
Hand-tuned score (dot product of above): -0.8368
Threshold: -2.0000

Inference ID: 12

Txt: Monet was born in Paris.

Hyp: Monet was born in Italy. (don't know)

Monet
NNP		 was
VBD		 born
VBN		 Italy
NNP
Monet:NNP   0.00 15.50 14.39   9.76
was:VBD 15.50   0.00   6.03 15.50
born:VBN 14.39   6.03   0.00 15.50
Paris:NNP   9.75 13.00 14.39   7.02
NO_WORD 10.00   1.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -1.76 Alignment.score
 1.00  0.36 Alignment.isGood
-1.00  0.06 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
Hand-tuned score (dot product of above): -0.9516
Threshold: -2.0000

Inference ID: 13

Txt: Talks will resume on August 14.

Hyp: Talks will resume in August. (yes)

Talks
NNS		 will
MD		 resume
VB		 August
NNP
Talks:NNS   0.00 18.89 15.00   8.81
will:MD 18.89   0.00 18.01 20.50
resume:VB 15.00 18.01   0.00 13.83
August:NNP   8.81 20.50 13.83   0.00
14:CD 20.50 20.50 20.32 20.00
NO_WORD 10.00 10.00 10.00 10.00

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.25 Alignment.score
 1.00  0.72 Alignment.isGood
-1.00  0.01 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 "14" of "August" dropped on aligned hyp word "August"
 1.00  1.00 Date.matchDatesByGraph : hyp/txt matching, by graph: August and children
-1.00  1.00 Structure.relMismatch : text "August" is prep_on of "resume" while hyp "August" is prep_in of "resume" which aligned to text "resume"
 0.50  1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.9571
Threshold: -2.0000

Inference ID: 14

Txt: Hiroshima was bombed on August 6.

Hyp: Hiroshima was bombed on August 8. (don't know)

Hiroshima
NNP		 was
VBD		 bombed
VBN		 August
NNP		 8
CD
Hiroshima:NNP   0.00 15.50 15.50   9.85 20.50
was:VBD 15.50   0.00   7.41 15.50 20.50
bombed:VBN 15.50   7.41   0.00 15.50 20.04
August_6:NNP   9.85 15.50 15.23   0.00 11.30
NO_WORD 10.00   1.00 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -2.00 Alignment.score
 1.00  0.31 Alignment.isGood
-1.00  0.08 Alignment.isBad
-0.10  1.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : the hypothesis added the modifier word "8"
-3.00  1.00 Date.dateHeadMismatch : August vs. August_6
-3.00  1.00 NullPunisher.entity : 8
Hand-tuned score (dot product of above): -8.3858
Threshold: -2.0000

Inference ID: 15

Txt: North Korea succeeded in testing a nuclear device.

Hyp: North Korea tested a nuclear device. (yes)

North_Korea
NNP		 tested
VBD		 a
DT		 nuclear
JJ		 device
NN
North_Korea:NNP   0.00 15.50 20.50 12.50 10.50
succeeded:VBD 15.50   6.79 20.00   9.50 15.00
in:IN 20.50 20.00 13.83 20.00 20.00
testing:VBG 15.50   0.00 20.00   8.23   8.26
a:DT 20.50 20.00   0.00 20.00 20.00
nuclear:JJ 12.50   8.96 20.00   0.00   9.68
device:NN 10.50   9.30 20.00   9.68   0.00
NO_WORD 10.00 10.00   1.00   9.00 10.00

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.40 Alignment.score
 1.00  0.69 Alignment.isGood
-1.00  0.02 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
 1.00  1.00 Quant.contract : [a,a]
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "North_Korea" <-nsubj-- "succeeded" vs. hyp "North_Korea" <-nsubj-- "tested", which aligned to text "testing"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.1463
Threshold: -2.0000

Inference ID: 16

Txt: The administration denied placing illegal wiretaps.

Hyp: The administration placed illegal wiretaps. (don't know)

The
DT		 administration
NN		 placed
VBD		 illegal
JJ		 wiretaps
NNS
The:DT   0.00 20.00 20.00 20.00 20.00
administration:NN 20.00   0.00 14.96   9.44   6.85
denied:VBD 20.00 14.35   6.67   7.80 12.56
placing:VBG 20.00 14.68   0.00   9.62 13.38
illegal:JJ 20.00   9.44 11.23   0.00   7.97
wiretaps:NNS 20.00   6.85 15.00   7.97   0.00
NO_WORD   1.00 10.00 10.00   9.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.40 Alignment.score
 1.00  0.69 Alignment.isGood
-1.00  0.02 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  5.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
-3.00  1.00 Structure.argsMismatch : args have different parents but same relations: text "administration" <-nsubj-- "denied" vs. hyp "administration" <-nsubj-- "placed", which aligned to text "placing"
-3.00  1.00 Structure.argsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -5.1263
Threshold: -2.0000

Inference ID: 17

Txt: Antonio Fazio, governor of the Bank of Italy, is engulfed in scandal.

Hyp: Antonio Fazio is governor of the Bank of Italy. (yes)

Antonio_Fazio
NNP		 is
VBZ		 governor
NN		 the
DT		 Bank_of_Italy
NNP
Antonio_Fazio:NNP   0.00 15.50 10.50 20.50   8.71
governor:NN   0.50 14.23   0.00 19.23   8.90
the:DT 20.50 20.00 20.00   0.00 20.50
Bank_of_Italy:NNP   8.71 15.50   8.90 20.50   0.00
is:VBZ 15.50   0.00 15.00 20.00 15.50
engulfed:VBN 15.50   6.35 14.95 20.00 15.50
scandal:NN 10.50 15.00   8.40 20.00   9.17
NO_WORD 10.00   1.00 10.00   1.00 10.00

Response: dontknow (INCORRECT)
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  1.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.80 Alignment.txtSpan
-0.05  1.00 NullPunisher.aux : is
-2.00  1.00 Structure.parentsMismatch : args have different parents, different relations: text "Antonio_Fazio" <-nsubjpass-- "engulfed" vs. hyp "Antonio_Fazio" <-nsubj-- "governor", which aligned to text "governor"
-3.00  1.00 Structure.parentsMismatch&Align.veryGood :
Hand-tuned score (dot product of above): -4.7442
Threshold: -2.0000

Inference ID: 18

Txt: Lincoln was assassinated by John Wilkes Booth.

Hyp: Lincoln assassinated John Wilkes Booth. (don't know)

Lincoln
NNP		 assassinated
VBD		 John_Wilkes_Booth
NNP
Lincoln:NNP   0.00 15.50   8.40
was:VBD 15.50   7.98 15.50
assassinated:VBN 15.50   0.00 15.50
John_Wilkes_Booth:NNP   8.40 15.50   0.00
NO_WORD 10.00 10.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -1.33 Alignment.score
 1.00  0.47 Alignment.isGood
-1.00  0.04 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
-3.00  1.00 Structure.clearBadness : for predicate assassinated, text actor set [John_Wilkes_Booth] overlaps hyp undergoer set [John_Wilkes_Booth]
-3.00  1.00 Structure.clearBadness : for predicate assassinated, text undergoer set [John_Wilkes_Booth] overlaps hyp actor set [John_Wilkes_Booth]
-1.00  1.00 Structure.relMismatch : text "Lincoln" is nsubjpass of "assassinated" while hyp "Lincoln" is nsubj of "assassinated" which aligned to text "assassinated" text "John_Wilkes_Booth" is agent of "assassinated" while hyp "John_Wilkes_Booth" is dobj of "assassinated" which aligned to text "assassinated"
Hand-tuned score (dot product of above): -7.5071
Threshold: -2.0000

Inference ID: 19

Txt: Spain sold warplanes to Venezuela.

Hyp: Venezuela bought warplanes from Spain. (yes)

Venezuela
NNP		 bought
VBD		 warplanes
NNS		 Spain
NNP
Spain:NNP   6.03 15.50   9.70   0.00
sold:VBD 15.50   0.00 15.00 14.39
warplanes:NNS   9.76 14.52   0.00   9.70
Venezuela:NNP   0.00 15.50   9.76   6.03
NO_WORD 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  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
Hand-tuned score (dot product of above): 0.0205
Threshold: -2.0000

Inference ID: 20

Txt: Brown criticized Blair sharply.

Hyp: Blair criticized Brown sharply. (don't know)

Blair
NNP		 criticized
VBD		 Brown
NNP		 sharply
RB
Brown:NNP   8.00 15.50   0.00 15.50
criticized:VBD 15.50   0.00 15.50 18.78
Blair:NNP   0.00 15.50   8.00 15.50
sharply:RB 15.50 18.78 15.50   0.00
NO_WORD 10.00 10.00 10.00   9.00

Response: dontknow (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  0.00 Alignment.nbWordNotAligned
 0.10  4.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
-3.00  1.00 Structure.clearBadness : for predicate criticized, text actor set [Brown] overlaps hyp undergoer set [Brown]
-3.00  1.00 Structure.clearBadness : for predicate criticized, text undergoer set [Brown] overlaps hyp actor set [Brown]
-1.00  1.00 Structure.relMismatch : text "Blair" is dobj of "criticized" while hyp "Blair" is nsubj of "criticized" which aligned to text "criticized" text "Brown" is nsubj of "criticized" while hyp "Brown" is dobj of "criticized" which aligned to text "criticized"
Hand-tuned score (dot product of above): -6.9795
Threshold: -2.0000

Inference ID: 21

Txt: Spain sells olive oil to Italy.

Hyp: Italy lost to Spain in the World Cup. (don't know)

Italy
NNP		 lost
VBD		 Spain
NNP		 the
DT		 World_Cup
NNP
Spain:NNP   3.17 15.50   0.00 20.50 10.50
sells:VBZ 13.50   7.68 13.50 20.00 15.50
olive_oil:NN 10.50 14.70 10.50 20.00   9.39
Italy:NNP   0.00 15.50   3.17 20.50 10.50
NO_WORD 10.00 10.00 10.00   1.00 10.00

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
 1.00 -4.54 Alignment.score
 1.00  0.03 Alignment.isGood
-1.00  0.51 Alignment.isBad
-0.10  2.00 Alignment.nbWordNotAligned
 0.10  3.00 Alignment.hypSpan
 0.10  0.40 Alignment.txtSpan
-1.00  1.00 Adjunct.addPosCxt : the hypothesis added the modifier word "World_Cup"
-0.10  1.00 NullPunisher.article : the
-3.00  1.00 NullPunisher.entity : World_Cup
-1.00  1.00 Structure.relMismatch : text "Italy" is prep_to of "sells" while hyp "Italy" is nsubj of "lost" which aligned to text "sells" text "Spain" is nsubj of "sells" while hyp "Spain" is prep_to of "lost" which aligned to text "sells"
Hand-tuned score (dot product of above): -9.9706
Threshold: -2.0000

Inference ID: 22

Txt: The bomb exploded on August 8 as experts had warned.

Hyp: The bomb exploded as experts had warned on August 8. (don't know)

The
DT		 bomb
NN		 exploded
VBD		 as
IN		 experts
NNS		 had
VBD		 warned
VBN		 August
NNP		 8
CD
The:DT   0.00 20.00 20.00 20.00 20.00 20.00 20.00 20.50 20.50
bomb:NN 20.00   0.00   8.15 20.00   8.03 15.00 12.52   9.66 19.31
exploded:VBD 20.00   8.15   0.00 20.00 11.67   8.06   7.91 15.50 19.80
August:NNP 20.50   9.66 15.50 20.50   8.11 15.50 15.50   0.00 20.00
8:CD 20.50 19.31 19.80 20.50 20.36 20.50 20.36 20.00   0.00
as:IN 20.00 20.00 20.00   0.00 20.00 18.00 20.00 20.50 20.50
experts:NNS 20.00   8.03 11.67 20.00   0.00 15.00 14.29   8.11 20.36
had:VBD 20.00 15.00   8.06 18.00 15.00   0.00   6.19 15.50 20.50
warned:VBN 20.00 12.52   7.91 20.00 14.29   6.19   0.00 15.50 20.36
NO_WORD   1.00 10.00 10.00 10.00 10.00   1.00 10.00 10.00 10.00

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
 1.00 -0.22 Alignment.score
 1.00  0.73 Alignment.isGood
-1.00  0.01 Alignment.isBad
-0.10  0.00 Alignment.nbWordNotAligned
 0.10  9.00 Alignment.hypSpan
 0.10  1.00 Alignment.txtSpan
 0.50  1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 08/08/1000
Hand-tuned score (dot product of above): 1.9908
Threshold: -2.0000

Hand-set weights Accuracy: 16/22 = 0.7273

Word similarity table built on Tue Mar 13 13:08:42 PDT 2007 using command:
java edu.stanford.nlp.rte.WordSimilarityGenerator -info /u/nlp/rte/data/byformat/align/simple/simple-test.align.xml -output /u/nlp/rte/data/byformat/wordsim/simple/simple-test.wordsim.html -alignmentWeights manual