Txt/Hyp term similarities

The rows are the txt words. The columns are hyp words.
Resource summary:
Acronym: AcronymLexicalResource
BasicWN: BasicWNLexicalResource
Country: CountryLexicalResource
Cyc: null
DekangLin: DekangLinLexicalResource
Google: null
InfoMap: InfoMapLexicalResource
NomBank: NomBankLexicalResource
Number: NumberLexicalResource
Ordinal: OrdinalLexicalResource
Preposition: PrepositionLexicalResource
Ravichandran: RavichandranLexicalResource
ResnikWN: ResnikWNLexicalResource
StringSim: StringSimLexicalResource

Inference ID: Cycorp-001

Txt: Sandy owns a Golden Retriever, Harley, with whom she has won a Dog World Award.

Hyp: Harley is a herding dog . (No.)

	Harley NNP	is VBZ	a DT	herding JJ	dog NN	. .
Sandy:NNP	9.96	15.46	20.50	12.46	10.46	20.50
owns:VBZ	15.46	10.00	20.00	12.00	14.72	19.98
a:DT	20.50	20.00	0.00	20.00	20.00	10.00
Golden_Retriever:NNP	14.96	14.84	20.50	12.46	3.88	20.50
,:,	20.50	20.00	10.00	20.00	18.12	5.73
Harley:NNP	0.00	15.46	20.50	12.46	10.46	20.50
,:,	20.50	20.00	10.00	20.00	18.12	5.73
with:IN	20.50	20.00	18.45	20.00	20.00	20.00
she:PRP	12.50	15.00	20.00	15.00	12.00	20.00
has:VBZ	15.46	8.64	20.00	12.00	14.34	20.00
won:VBN	15.46	10.00	20.00	11.04	15.00	19.64
a:DT	20.50	20.00	0.00	20.00	20.00	10.00
Dog:NNP	14.96	12.84	20.50	12.50	0.50	20.50
World:NNP	14.96	14.84	20.50	10.63	9.45	20.50
Award:NNP	14.96	15.50	20.50	12.50	10.50	20.50
.:.	20.50	20.00	10.00	19.45	19.56	0.00
NO_WORD	10.00	1.00	1.00	9.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -15.5000
Features matched: Adjunct.addPosCxt: hyp added herding[herding-JJ]; Adjunct.dropPosCxt: text adjunct "won" of "Harley" dropped on aligned hyp word "Harley"; NullPunisher.aux: is; NullPunisher.other: herding; NullPunisher.article: a; Structure.argsMismatch: args have different parents but same relations: text "Harley" <-appos-- "Golden_Retriever vs. hyp "Harley" <-nsubj-- "dog", which aligned to text "Dog" args have different parents but same relations: text "." <-punct-- "owns vs. hyp "." <-punct-- "dog", which aligned to text "Dog" args have different parents, different relations: text "Harley" <-dep-- "with" vs. hyp "Harley" <-nsubj-- "dog", which aligned to text "Dog"
Hand-tuned score: -3.6500
Threshold: -11.4590

Inference ID: Cycorp-002

Txt: Many cellphones have built-in digital cameras.

Hyp: Some cellphones can be used to take pictures . (Yes.)

	Some DT	cellphones NNS	can MD	be VB	used VBN	to TO	take VB	pictures NNS	. .
Many:JJ	20.00	11.96	19.96	11.96	11.96	20.00	11.96	11.96	20.00
cellphones:NNS	20.00	0.00	17.12	14.34	15.00	20.00	15.00	8.03	20.00
have:VBP	20.00	13.95	17.32	7.80	6.55	20.00	1.83	12.32	20.00
built-in:JJ	20.00	11.96	19.96	11.96	11.96	20.00	11.96	11.96	20.00
digital_cameras:NNS	20.00	5.06	17.12	14.34	14.96	20.00	14.96	8.03	20.00
.:.	10.00	20.00	10.00	20.00	18.25	10.00	17.67	18.10	0.00
NO_WORD	10.00	10.00	10.00	1.00	10.00	10.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -51.4203
Features matched: Adjunct.dropPosCxt: text adjunct "built-in" of "digital_cameras" dropped on aligned hyp word "pictures"; Modal.yes: actual -> possible; NullPunisher.functionWord: to; NullPunisher.aux: be; NullPunisher.other: Some; NullPunisher.aux: can; Quant.contract: [many,some]; RootEntailment.poorlyAlignedRoot: "used" aligned badly to "have"; Structure.relMismatch: text "cellphones" is nsubj of "have" while hyp "cellphones" is nsubjpass of "used" which aligned to text "have"
Hand-tuned score: 0.3000
Threshold: -11.4590

Inference ID: Cycorp-003

Txt: One in four cellphones sold has a camera in it.

Hyp: Do most cell_phones have a lens . (No.)

	Do VB	most JJS	cell_phones NNS	have VB	a DT	lens NN	. .
One:CD	19.19	20.46	19.84	20.50	20.50	20.50	20.50
four:CD	19.19	20.46	19.84	20.50	20.50	20.50	19.35
cellphones:NNS	15.00	11.96	1.90	13.95	20.00	3.53	20.00
sold:VBN	7.69	11.96	12.80	5.68	20.00	13.35	19.42
has:VBZ	7.53	11.96	14.34	0.50	20.00	14.34	20.00
a:DT	20.00	20.00	20.00	20.00	0.00	20.00	10.00
camera:NN	15.00	11.96	6.77	13.95	20.00	3.53	18.65
it:PRP	15.00	15.00	12.00	15.00	20.00	12.00	20.00
.:.	20.00	20.00	20.00	20.00	10.00	20.00	0.00
NO_WORD	10.00	9.00	10.00	10.00	1.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -26.4626
Features matched: Adjunct.addPosCxt: hyp added most[most-JJS]; Adjunct.dropPosCxt: text adjunct "it" of "camera" dropped on aligned hyp word "lens"; Polarity.hypNegMarker: "most": JJS; NullPunisher.other: most; Quant.contract: [a,a]; RootEntailment.poorlyAlignedRoot: "Do" aligned badly to "has"
Hand-tuned score: -1.5000
Threshold: -11.4590

Inference ID: Cycorp-004

Txt: Three in four cellphones sold has a camera in it.

Hyp: Do most cell_phones have a lens . (Yes.)

	Do VB	most JJS	cell_phones NNS	have VB	a DT	lens NN	. .
Three:CD	19.19	20.46	19.84	20.50	20.50	20.50	20.50
four:CD	19.19	20.46	19.84	20.50	20.50	20.50	19.35
cellphones:NNS	15.00	11.96	1.90	13.95	20.00	3.53	20.00
sold:VBN	7.69	11.96	12.80	5.68	20.00	13.35	19.42
has:VBZ	7.53	11.96	14.34	0.50	20.00	14.34	20.00
a:DT	20.00	20.00	20.00	20.00	0.00	20.00	10.00
camera:NN	15.00	11.96	6.77	13.95	20.00	3.53	18.65
it:PRP	15.00	15.00	12.00	15.00	20.00	12.00	20.00
.:.	20.00	20.00	20.00	20.00	10.00	20.00	0.00
NO_WORD	10.00	9.00	10.00	10.00	1.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -26.4626
Features matched: Adjunct.addPosCxt: hyp added most[most-JJS]; Adjunct.dropPosCxt: text adjunct "it" of "camera" dropped on aligned hyp word "lens"; Polarity.hypNegMarker: "most": JJS; NullPunisher.other: most; Quant.contract: [a,a]; RootEntailment.poorlyAlignedRoot: "Do" aligned badly to "has"
Hand-tuned score: -1.5000
Threshold: -11.4590

Inference ID: Cycorp-005

Txt: Mr. Radley ordered a 16 ounce slab of slowly roasted Black Angus Prime Rib.

Hyp: Radley is a vegetarian . (No.)

	Radley NNP	is VBZ	a DT	vegetarian NN	. .
Mr._Radley:NNP	0.00	15.46	20.50	10.46	20.50
ordered:VBD	15.46	7.74	20.00	15.00	20.00
a:DT	20.50	20.00	0.00	20.00	10.00
16:CD	24.96	20.50	20.50	19.80	19.82
ounce:NN	10.46	14.34	20.00	8.11	19.86
slab:NN	10.46	14.34	20.00	8.03	20.00
slowly:RB	15.46	19.96	20.00	14.96	18.14
roasted:JJ	12.46	9.34	20.00	7.83	20.00
Black_Angus_Prime_Rib:NNP	14.96	14.17	20.50	9.52	20.50
.:.	20.50	20.00	10.00	20.00	0.00
NO_WORD	10.00	1.00	1.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -13.0325
Features matched: Adjunct.dropPosCxt: text adjunct "Black_Angus_Prime_Rib" of "slab" dropped on aligned hyp word "vegetarian"; NullPunisher.aux: is; Quant.contract: [a,a]; RootEntailment.poorlyAlignedRoot: "vegetarian" aligned badly to "slab"; Structure.argsMismatch: args have different parents but same relations: text "Mr._Radley" <-nsubj-- "ordered vs. hyp "Radley" <-nsubj-- "vegetarian", which aligned to text "slab" args have different parents but same relations: text "." <-punct-- "ordered vs. hyp "." <-punct-- "vegetarian", which aligned to text "slab"
Hand-tuned score: -2.5500
Threshold: -11.4590

Inference ID: Cycorp-006

Txt: Sue ran down to McDonald's and got a hamburger happy meal with a large Diet Coke.

Hyp: Sue did get a cup . (Yes.)

	Sue NNP	did VBD	get VB	a DT	cup NN	. .
Sue:NNP	0.00	15.00	12.65	20.00	7.65	20.00
ran_down:VBD	13.82	7.45	3.85	20.00	12.62	20.00
McDonald:NNP	10.46	15.46	15.46	20.50	10.46	20.50
got:VBD	12.65	5.62	0.50	20.00	12.62	17.75
a:DT	20.00	20.00	20.00	0.00	20.00	10.00
hamburger:RB	14.34	18.41	18.31	20.00	10.84	20.00
happy:JJ	11.96	11.96	6.05	20.00	11.42	17.14
meal:NN	9.34	13.69	12.37	20.00	5.84	19.61
a:DT	20.00	20.00	20.00	0.00	20.00	10.00
large:JJ	12.00	9.53	12.00	20.00	8.55	18.45
Diet_Coke:NN	9.54	15.46	13.79	20.50	7.82	20.50
.:.	20.00	17.99	17.78	10.00	20.00	0.00
NO_WORD	10.00	1.00	10.00	1.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -11.3413
Features matched: Adjunct.dropPosCxt: text adjunct "happy" of "meal" dropped on aligned hyp word "cup"; NullPunisher.aux: did; Quant.contract: [a,a]; Structure.argsMismatch: args have different parents but same relations: text "Sue" <-nsubj-- "ran_down vs. hyp "Sue" <-nsubj-- "get", which aligned to text "got" args have different parents but same relations: text "." <-punct-- "ran_down vs. hyp "." <-punct-- "get", which aligned to text "got"
Hand-tuned score: -0.5500
Threshold: -11.4590

Inference ID: Cycorp-007

Txt: Angela Drake will be moving to California with her husband, where she has accepted a full-time position with the San Diego Public Library.

Hyp: Angela Drake does live in Los_Angeles . (No.)

	Angela_Drake NNP	does VBZ	live VB	Los_Angeles NNP	. .
Angela_Drake:NNP	0.00	13.23	15.46	10.46	20.50
will:MD	20.46	20.00	20.00	19.96	10.00
be:VB	15.17	9.34	1.00	14.34	20.00
moving:VBG	15.46	10.00	4.03	14.96	17.38
California:NNP	14.67	14.84	15.50	2.50	20.50
her:PRP$	12.50	15.00	15.00	12.00	20.00
husband:NN	9.52	13.11	13.53	9.34	19.33
,:,	20.50	19.26	17.82	20.00	5.73
where:WRB	20.46	19.96	19.96	19.96	10.00
she:PRP	12.50	15.00	15.00	12.00	20.00
has:VBZ	15.17	9.34	10.00	12.52	20.00
accepted:VBN	15.46	10.00	8.07	14.96	17.53
a:DT	20.50	20.00	20.00	20.00	10.00
full-time:JJ	12.46	11.96	10.88	11.96	17.39
position:NN	10.17	14.34	15.00	6.03	18.83
the:DT	20.50	18.65	20.00	20.00	10.00
San_Diego_Public_Library:NNP	14.47	14.41	15.46	10.46	20.50
.:.	20.50	20.00	18.97	20.00	0.00
NO_WORD	10.00	1.00	10.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -12.8582
Features matched: Adjunct.dropPosCxt: text adjunct "husband" of "moving" dropped on aligned hyp word "live"; NullPunisher.aux: does; RootEntailment.poorlyAlignedRoot: "live" aligned badly to "moving"; Structure.relMismatch: text "California" is prep_to of "moving" while hyp "Los_Angeles" is prep_in of "live" which aligned to text "moving"
Hand-tuned score: -1.5500
Threshold: -11.4590

Inference ID: Cycorp-008

Txt: Ambassador Richard C. Holbrooke will visit Paris, France from Saturday, April 22 until Tuesday, April 25.

Hyp: Ambassador Holbrooke will visit France in April . (Yes.)

	Ambassador NNP	Holbrooke NNP	will MD	visit VB	France NNP	April NNP	. .
Ambassador:NNP	0.00	9.29	20.00	15.00	8.55	10.50	20.00
Richard_C._Holbrooke:NNP	10.46	0.00	20.46	15.46	14.96	14.96	20.50
will:MD	20.00	20.46	0.00	20.00	20.50	19.19	10.00
visit:VB	15.00	15.46	20.00	0.00	15.50	15.50	20.00
Paris:NNP	9.84	14.96	20.50	13.63	2.00	15.00	20.50
,:,	20.50	25.00	10.50	20.50	25.00	20.00	6.23
France:NNP	8.55	14.96	20.50	15.50	0.00	15.00	20.50
Saturday:NNP	10.50	14.96	19.19	15.50	15.00	7.23	20.50
,:,	20.50	25.00	10.50	20.50	25.00	20.00	6.23
April:NNP	10.50	14.96	19.19	15.50	15.00	0.00	20.50
22:CD	20.50	24.96	19.19	20.50	25.00	17.84	19.32
Tuesday:NNP	10.50	14.96	19.19	15.50	15.00	7.23	20.50
,:,	20.50	25.00	10.50	20.50	25.00	20.00	6.23
April:NNP	10.50	14.96	19.19	15.50	15.00	0.00	20.50
25:CD	20.50	24.96	19.19	20.50	25.00	17.84	19.52
.:.	20.00	20.50	10.00	20.00	20.50	20.50	0.00
NO_WORD	10.00	10.00	10.00	10.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -4.0000
Features matched: Adjunct.dropPosCxt: text adjunct "25" of "April" dropped on aligned hyp word "April"; Date.matchDatesByGraph: hyp/txt matching, by graph: April and children; Structure.parentsMismatch: args have different parents, different relations: text "France" <-appos-- "Paris" vs. hyp "France" <-dobj-- "visit", which aligned to text "visit"
Hand-tuned score: -0.5000
Threshold: -11.4590

Inference ID: Cycorp-009

Txt: A full-time administrative assistant, Janet Smith lives close to Long Beach, where she's worked for the past 12 years.

Hyp: Janet Smith does live in Long_Beach . (No.)

	Janet_Smith NNP	does VBZ	live VB	Long_Beach NNP	. .
A:DT	20.50	20.00	20.00	20.00	10.00
full-time:JJ	12.46	11.96	10.88	11.96	17.39
administrative:JJ	12.46	10.80	11.96	11.96	19.85
assistant:NN	8.61	13.11	15.00	9.34	20.00
,:,	20.50	19.26	17.82	20.00	5.73
Janet_Smith:NNP	0.00	14.55	15.46	9.97	20.50
lives:VBZ	14.52	8.11	0.50	13.62	18.56
Long_Beach:NNP	14.47	14.84	15.50	0.50	20.50
,:,	20.50	19.26	17.82	20.00	5.73
where:WRB	20.46	19.96	19.96	19.96	10.00
she:PRP	12.50	15.00	15.00	12.00	20.00
's:VBZ	15.17	6.09	9.76	13.92	18.25
worked:VBN	14.97	8.95	8.07	12.52	18.80
the:DT	20.50	18.65	20.00	20.00	10.00
past:JJ	12.46	10.13	12.00	10.62	19.32
12:CD	24.96	20.50	19.17	19.42	19.69
years:NNS	10.46	15.00	14.82	6.52	19.49
.:.	20.50	20.00	18.97	20.00	0.00
NO_WORD	10.00	1.00	10.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -5.0000
Features matched: NullPunisher.aux: does; Structure.argsMismatch: args have different parents but same relations: text "Janet_Smith" <-appos-- "assistant vs. hyp "Janet_Smith" <-nsubj-- "live", which aligned to text "lives" text "Long_Beach" is prep_close_to of "lives" while hyp "Long_Beach" is prep_in of "live" which aligned to text "lives"
Hand-tuned score: -2.0500
Threshold: -11.4590

Inference ID: Cycorp-010

Txt: Bob Appleton, 33, lives in Sacramento but commutes to Davis in his silver Honda CRV.

Hyp: Appleton does have a California Driver 's license . (Yes.)

	Appleton NNP	does VBZ	have VB	a DT	California_Driver NNP	license NN	. .
Bob_Appleton:NNP	5.00	14.55	14.31	20.50	13.17	9.68	20.50
,:,	20.50	19.26	20.00	10.00	20.50	19.79	5.73
33:CD	24.96	20.46	20.46	20.50	24.96	20.46	18.52
,:,	20.50	19.26	20.00	10.00	20.50	19.79	5.73
lives:VBZ	13.55	8.11	8.05	20.00	14.42	12.44	18.56
Sacramento:NNP	10.69	14.84	14.84	20.50	13.17	10.50	20.50
commutes:NNP	10.50	14.05	12.32	20.00	10.46	10.00	20.00
Davis:NNP	13.05	13.61	13.55	20.50	12.04	10.50	20.50
his:PRP$	12.50	15.00	15.00	20.00	12.50	12.00	20.00
silver:JJ	11.45	10.95	9.61	20.00	11.42	9.85	18.95
Honda_CRV:NN	9.96	15.46	15.46	20.50	9.96	10.46	20.50
.:.	20.50	20.00	20.00	10.00	20.50	19.98	0.00
NO_WORD	10.00	1.00	10.00	1.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -35.0496
Features matched: Adjunct.dropPosCxt: text adjunct "Davis" of "lives" dropped on aligned hyp word "have"; NullPunisher.article: a; NullPunisher.aux: does; NullPunisher.other: California_Driver; NullPunisher.other: license; RootEntailment.poorlyAlignedRoot: "have" aligned badly to "lives"
Hand-tuned score: -2.6500
Threshold: -11.4590

Inference ID: Cycorp-011

Txt: When they remodeled their house, the Kirchners transformed the original kitchen into a game room with dark cherry laminate flooring and cabinets.

Hyp: Does the Krichners ' kitchen have cherry cabinets ? (Unknown.)

	Does RB	the DT	Krichners NNPS	kitchen NN	have VBP	cherry JJ	cabinets NNS	? .
When:WRB	19.96	10.00	20.46	19.96	19.96	19.96	19.96	10.00
they:PRP	20.00	15.71	12.50	12.00	15.00	15.00	12.00	20.00
remodeled:VBD	20.00	20.00	15.46	11.05	7.62	9.51	11.30	20.00
their:PRP$	20.00	20.00	12.50	12.00	15.00	15.00	12.00	20.00
house:NN	11.69	20.00	10.46	6.19	13.95	10.29	6.66	20.00
,:,	20.00	10.00	20.50	18.54	20.00	17.15	18.85	10.00
the:DT	20.00	0.00	20.50	20.00	20.00	20.00	20.00	10.00
Kirchners:NNPS	13.61	20.50	9.44	9.45	13.55	10.61	9.45	20.50
transformed:VBD	20.00	20.00	15.46	15.00	7.61	12.00	15.00	19.73
the:DT	20.00	0.00	20.50	20.00	20.00	20.00	20.00	10.00
original:JJ	10.95	20.00	12.46	10.03	10.95	8.95	10.03	19.83
kitchen:NN	13.95	20.00	10.46	0.00	13.95	7.60	3.12	19.98
a:DT	20.00	10.00	20.50	20.00	20.00	20.00	20.00	10.00
game_room:NN	12.88	20.00	10.46	5.46	13.95	9.27	5.61	20.00
dark:JJ	11.34	20.00	12.46	6.73	11.34	5.26	8.72	20.00
cherry:JJ	10.11	20.00	12.46	7.60	10.11	0.00	7.97	19.72
laminate:JJ	10.95	20.00	12.46	8.72	9.62	6.18	7.69	18.60
flooring:NNS	13.95	20.00	10.46	5.75	13.95	8.75	5.08	20.00
cabinets:NNS	10.85	20.00	10.46	3.12	13.95	7.97	0.00	19.54
.:.	20.00	10.00	20.50	18.73	20.00	19.85	19.19	10.00
NO_WORD	9.00	1.00	10.00	10.00	10.00	9.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -43.6142
Features matched: Adjunct.addPosCxt: hyp added Does[Does-RB]; Adjunct.dropPosCxt: text adjunct "flooring" of "transformed" dropped on aligned hyp word "have"; NullPunisher.other: Does; NullPunisher.other: ?; NullPunisher.other: Krichners; NullPunisher.article: the; RootEntailment.poorlyAlignedRoot: "have" aligned badly to "transformed"; Structure.relMismatch: text "kitchen" is dobj of "transformed" while hyp "kitchen" is nsubj of "have" which aligned to text "transformed" text "cabinets" is prep_with of "transformed" while hyp "cabinets" is dobj of "have" which aligned to text "transformed"
Hand-tuned score: -5.6000
Threshold: -11.4590

Inference ID: Cycorp-012

Txt: Mark Jacobs, who lives in Edmonds and works in Seattle, spends 2 hours a day commuting.

Hyp: Jacobs does commute to work during rush_hour . (Yes.)

	Jacobs NNP	does VBZ	commute NN	to TO	work VB	rush_hour NN	. .
Mark_Jacobs:NNS	0.00	14.55	9.16	20.50	13.23	7.50	20.50
,:,	20.50	19.26	20.00	10.00	19.61	20.00	5.73
who:WP	12.50	15.00	12.00	20.00	15.00	12.00	20.00
lives:VBZ	13.55	8.11	10.16	20.00	6.27	12.82	18.56
Edmonds:NNP	14.96	15.46	10.46	20.50	15.46	10.46	20.50
works:VBZ	14.45	8.42	13.82	20.00	0.50	14.18	19.16
Seattle:NNP	14.34	14.84	10.50	20.50	10.96	10.17	20.50
,:,	20.50	19.26	20.00	10.00	19.61	20.00	5.73
spends:VBZ	15.50	8.12	11.68	20.00	7.78	15.00	20.00
2:CD	25.00	20.50	20.50	20.50	20.41	19.42	18.58
hours:NNS	10.50	15.00	6.02	20.00	11.60	5.00	19.95
a:DT	20.50	20.00	20.00	10.00	20.00	20.00	10.00
day:NN	4.79	13.11	7.09	20.00	12.57	8.01	18.14
commuting:VBG	15.50	10.00	0.50	20.00	3.90	13.04	20.00
.:.	20.50	20.00	19.75	10.00	18.57	20.00	0.00
NO_WORD	10.00	10.00	10.00	10.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -30.1176
Features matched: Adjunct.dropPosCxt: text adjunct "2" of "hours" dropped on aligned hyp word "rush_hour"; NullPunisher.functionWord: to; RootEntailment.poorlyAlignedRoot: "does" aligned badly to "spends"; Structure.parentsMismatch: args have different parents, different relations: text "commuting" <-partmod-- "hours" vs. hyp "commute" <-dobj-- "does", which aligned to text "spends" args have different parents, different relations: text "works" <-rcmod-- "Mark_Jacobs" vs. hyp "work" <-xcomp-- "does", which aligned to text "spends"
Hand-tuned score: -3.6000
Threshold: -11.4590

Inference ID: Cycorp-013

Txt: Terry Parks married Robert Paulson in 1979.

Hyp: Terry Parks is a man . (No.)

	Terry_Parks NNS	is VBZ	a DT	man NN	. .
Terry_Parks:NNS	0.00	15.17	20.50	9.52	20.50
married:VBD	15.46	8.07	20.00	10.61	19.71
Robert_Paulson:NNP	9.02	15.17	20.50	9.52	20.50
1979:CD	24.96	20.46	20.50	18.79	19.57
.:.	20.50	20.00	10.00	19.76	0.00
NO_WORD	10.00	1.00	1.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -12.6122
Features matched: NullPunisher.aux: is; NullPunisher.article: a; RootEntailment.poorlyAlignedRoot: "man" aligned badly to "married"
Hand-tuned score: -1.1500
Threshold: -11.4590

Inference ID: Cycorp-014

Txt: The Paulsons celebrated their 25th anniversary on June 14, 2004.

Hyp: The Paulsons did get_married on Flag_Day . (MULTIPLE ANSWERS)

	The DT	Paulsons NNPS	did VBD	get_married VBN	Flag_Day NNP	. .
The:DT	0.00	20.50	20.00	20.00	20.00	10.00
Paulsons:NNPS	20.50	0.00	15.46	15.46	10.46	20.50
celebrated:VBD	20.00	15.46	9.33	10.00	15.00	20.00
their:PRP$	20.00	12.50	15.00	15.00	12.00	20.00
25th:JJ	20.00	12.46	11.52	11.96	11.96	20.00
anniversary:NN	20.00	10.46	13.69	15.00	4.16	20.00
June_14:NNP	20.50	14.96	14.19	15.50	0.50	20.50
,:,	10.50	25.00	20.30	20.50	20.50	6.23
2004:CD	20.50	24.96	20.46	20.46	20.46	20.50
.:.	10.00	20.50	17.99	20.00	20.00	0.00
NO_WORD	1.00	10.00	1.00	10.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -11.8094
Features matched: Adjunct.dropPosCxt: text adjunct "2004" of "June_14" dropped on aligned hyp word "Flag_Day"; NullPunisher.aux: did; RootEntailment.poorlyAlignedRoot: "get_married" aligned badly to "celebrated"
Hand-tuned score: -0.5500
Threshold: -11.4590

Inference ID: Cycorp-015

Txt: The Island Nut Sampler includes an 8 ounce box of milk chocolate covered macadamia nuts and an 8 ounce box of white chocolate covered macadamia nuts.

Hyp: It does include a pound of macadamia nuts . (Yes.)

	It PRP	does VBZ	include VB	a DT	pound NN	macadamia NN	nuts NNS	. .
The:DT	20.00	20.00	20.00	10.00	20.00	20.00	20.00	10.00
Island:NNP	12.50	14.45	15.50	20.50	9.45	9.45	9.45	20.50
Nut:NNP	12.50	13.61	15.50	20.50	8.34	8.61	0.81	20.50
Sampler:NNP	12.50	13.61	15.50	20.50	8.53	8.61	8.53	20.50
includes:VBZ	15.00	9.55	0.50	20.00	12.61	14.87	15.00	19.56
an:DT	20.00	17.89	20.00	8.73	20.00	20.00	20.00	10.00
8:CD	20.50	20.50	20.50	20.50	16.96	20.50	18.34	19.96
ounce:NN	12.00	10.17	15.00	20.00	0.94	8.11	7.84	19.86
box:NN	12.00	13.08	12.42	20.00	3.45	4.40	7.81	19.82
milk_chocolate:NN	12.00	14.34	12.87	20.00	8.81	8.53	8.44	20.00
covered:VBN	15.00	8.49	5.64	20.00	12.45	13.95	11.46	19.33
macadamia:NN	12.00	13.11	13.91	20.00	8.11	0.00	5.49	19.94
nuts:NNS	12.00	13.11	15.00	20.00	7.07	5.49	0.00	19.80
an:DT	20.00	17.89	20.00	8.73	20.00	20.00	20.00	10.00
8:CD	20.50	20.50	20.50	20.50	16.96	20.50	18.34	19.96
ounce:NN	12.00	10.17	15.00	20.00	0.94	8.11	7.84	19.86
box:NN	12.00	13.08	12.42	20.00	3.45	4.40	7.81	19.82
white_chocolate:NN	12.00	14.05	13.81	20.00	7.15	9.05	8.44	20.00
covered:VBD	15.00	8.49	5.64	20.00	12.45	13.95	11.46	19.33
macadamia:NN	12.00	13.11	13.91	20.00	8.11	0.00	5.49	19.94
nuts:NNS	12.00	13.11	15.00	20.00	7.07	5.49	0.00	19.80
.:.	20.00	20.00	19.86	10.00	19.64	19.94	19.80	0.00
NO_WORD	10.00	1.00	10.00	1.00	10.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -17.4408
Features matched: NullPunisher.aux: does; NullPunisher.other: It; NullPunisher.article: a; Structure.parentsMismatch: args have different parents, different relations: text "ounce" <-nn-- "box" vs. hyp "pound" <-dobj-- "include", which aligned to text "includes"
Hand-tuned score: -3.1500
Threshold: -11.4590

Inference ID: Cycorp-016

Txt: Nanolab says it can't stay profitable if the demand for nanotubes decreases.

Hyp: Nanolab does manufacture nanotubes . (MULTIPLE ANSWERS)

	Nanolab NNP	does VBZ	manufacture NN	nanotubes NNS	. .
Nanolab:NNP	0.00	14.96	9.96	9.96	20.00
says:VBZ	14.96	9.34	14.82	15.00	18.68
it:PRP	12.00	15.00	12.00	12.00	20.00
ca:MD	19.96	16.22	19.83	15.05	9.11
n't:RB	14.96	17.14	13.59	14.96	17.90
stay:VB	14.96	8.33	13.35	14.34	18.79
profitable:JJ	11.96	11.96	9.71	11.96	19.77
if:IN	20.00	16.87	20.00	20.00	20.00
the:DT	20.00	18.65	20.00	20.00	10.00
demand:NN	9.96	15.00	7.10	10.00	20.00
nanotubes:NNS	9.96	14.34	10.00	0.00	20.00
decreases:VBZ	14.96	10.00	12.69	15.00	19.25
.:.	20.00	20.00	19.10	20.00	0.00
NO_WORD	10.00	10.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -20.4384
Features matched: RootEntailment.poorlyAlignedRoot: "does" aligned badly to "says"; Structure.parentsMismatch: args have different parents, different relations: text "nanotubes" <-prep_for-- "demand" vs. hyp "nanotubes" <-dobj-- "does", which aligned to text "says"
Hand-tuned score: -4.0000
Threshold: -11.4590

Inference ID: Cycorp-017

Txt: The Swedish Embassy in Bangkok will be closed April 13-17 during the Songkran Festival.

Hyp: The Songkran Festival is a Swedish holiday . (Unknown.)

	The DT	Songkran_Festival NNP	is VBZ	a DT	Swedish JJ	holiday NN	. .
The:DT	0.00	20.50	20.00	10.00	20.50	20.00	10.00
Swedish:NNP	20.50	14.34	15.50	20.50	0.00	9.19	20.50
Embassy:NNP	20.50	14.96	14.84	20.50	12.00	10.50	20.50
Bangkok:NNP	20.50	14.96	14.84	20.50	17.00	10.50	20.50
will:MD	10.00	19.84	20.00	10.00	18.35	18.69	10.00
be:VB	20.00	15.46	0.31	20.00	12.50	15.00	20.00
closed:VBN	20.00	14.42	8.07	20.00	10.35	12.84	19.49
April:NNP	20.50	13.62	15.50	20.50	15.69	7.73	20.50
13-17:CD	20.50	24.96	20.46	20.50	24.96	20.24	19.37
the:DT	0.00	20.50	20.00	10.00	20.50	20.00	10.00
Songkran_Festival:NNP	20.50	0.00	15.46	20.50	16.34	9.12	20.50
.:.	10.00	20.50	20.00	10.00	20.50	19.99	0.00
NO_WORD	1.00	10.00	1.00	1.00	9.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -15.7318
Features matched: Adjunct.dropPosCxt: text adjunct "13-17" of "April" dropped on aligned hyp word "holiday"; NullPunisher.aux: is; NullPunisher.article: a; RootEntailment.poorlyAlignedRoot: "holiday" aligned badly to "April"; Structure.argsMismatch: args have different parents but same relations: text "." <-punct-- "closed vs. hyp "." <-punct-- "holiday", which aligned to text "April" args have different parents, different relations: text "Songkran_Festival" <-prep_during-- "closed" vs. hyp "Songkran_Festival" <-nsubj-- "holiday", which aligned to text "April" args have different parents, different relations: text "Swedish" <-nn-- "Embassy" vs. hyp "Swedish" <-amod-- "holiday", which aligned to text "April" noun args have different parents but same relations: "Swedish": "Embassy" vs. "holiday"
Hand-tuned score: -3.6500
Threshold: -11.4590

Inference ID: Cycorp-018

Txt: The Nut Sampler set includes two 8 ounce boxes of chocolate covered nuts.

Hyp: This set does contain milk_chocolate covered nuts . (Unknown.)

	This DT	set NN	does VBZ	contain VB	milk_chocolate JJ	covered VBN	nuts NNS	. .
The:DT	10.00	20.00	20.00	20.00	20.00	20.00	20.00	10.00
Nut_Sampler:NNP	20.50	5.65	14.55	15.46	10.94	13.73	5.65	20.50
set:NN	20.00	0.00	12.81	12.79	10.81	6.83	0.31	18.94
includes:VBZ	20.00	11.46	9.55	6.26	9.87	5.64	15.00	19.56
two:CD	20.50	18.34	20.50	19.56	19.84	19.92	18.34	19.42
8:CD	20.50	18.34	20.50	20.17	19.84	19.42	18.34	19.96
ounce:NN	20.00	7.84	10.17	14.37	11.34	13.95	7.84	19.86
boxes:NNS	20.00	7.12	13.11	12.66	10.76	9.85	7.84	18.97
chocolate:NN	20.00	8.69	14.34	14.77	2.00	11.75	3.08	19.54
covered:VBN	20.00	6.83	8.49	4.58	10.81	0.00	11.46	19.33
nuts:NNS	20.00	0.31	13.11	15.00	10.44	11.46	0.00	19.80
.:.	10.00	18.94	20.00	18.50	20.00	19.33	19.80	0.00
NO_WORD	10.00	10.00	1.00	10.00	9.00	10.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -23.2614
Features matched: Adjunct.dropPosCxt: text adjunct "Nut_Sampler" of "set" dropped on aligned hyp word "set"; NullPunisher.aux: does; NullPunisher.other: This; RootEntailment.poorlyAlignedRoot: "contain" aligned badly to "includes"; Structure.parentsMismatch: args have different parents, different relations: text "nuts" <-prep_of-- "boxes" vs. hyp "nuts" <-dobj-- "contain", which aligned to text "includes"
Hand-tuned score: -4.5500
Threshold: -11.4590

Inference ID: Cycorp-019

Txt: The Mini Mac was introduced by Apple CEO Steve Jobs at his keynote address on January 11.

Hyp: Apple Computer did release a new Macintosh in January . (Yes.)

	Apple_Computer NNP	did VBD	release VB	a DT	new JJ	Macintosh NNP	January NNP	. .
The:DT	20.50	20.00	20.00	10.00	20.00	20.50	20.50	10.00
Mini_Mac:NNP	9.02	15.46	13.47	20.50	12.46	5.42	14.96	20.50
was:VBD	15.17	10.00	9.34	20.00	11.96	14.84	15.50	20.00
introduced:VBN	15.46	6.26	6.87	20.00	10.51	15.50	15.50	20.00
Apple:NNP	0.00	15.50	14.45	20.50	12.46	8.95	15.00	20.50
CEO:NNP	9.52	15.00	13.95	20.00	11.96	9.45	10.50	20.00
Steve_Jobs:NNP	14.02	14.34	13.89	20.50	12.46	14.02	14.34	20.50
his:PRP$	12.50	15.00	15.00	20.00	15.00	12.50	12.50	20.00
keynote_address:NN	10.17	13.38	13.35	20.00	11.96	10.17	9.84	20.00
January:NNP	14.96	14.19	14.19	20.50	12.46	15.00	0.00	20.50
11:CD	24.96	19.19	19.19	20.50	20.30	25.00	17.84	19.37
.:.	20.50	17.99	19.68	10.00	20.00	20.50	20.50	0.00
NO_WORD	10.00	1.00	10.00	1.00	9.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -26.2923
Features matched: Adjunct.addPosCxt: hyp added new[new-JJ]; Adjunct.dropPosCxt: text adjunct "11" of "January" dropped on aligned hyp word "January"; Date.matchDatesByGraph: hyp/txt matching, by graph: January and children; NullPunisher.article: a; NullPunisher.other: new; NullPunisher.aux: did; Quant.contract: [the,a]; RootEntailment.poorlyAlignedRoot: "release" aligned badly to "introduced"; Structure.relMismatch: text "January" is prep_on of "introduced" while hyp "January" is prep_in of "release" which aligned to text "introduced"
Hand-tuned score: -1.6500
Threshold: -11.4590

Inference ID: Cycorp-020

Txt: The Donald criticized President Bush over his decision to go to war with Iraq.

Hyp: Trump does support W 's decision to go_to_war . (No.)

	Trump NNP	does VBZ	support VB	W NNP	decision NN	go_to_war NN	. .
The:DT	20.50	20.00	20.00	20.00	20.00	20.00	10.00
Donald:NNP	14.96	15.46	15.46	10.46	10.46	10.46	20.50
criticized:VBD	15.50	10.00	6.01	15.00	13.36	14.96	19.96
President_Bush:NNP	9.45	13.11	13.51	9.34	9.18	8.86	20.00
his:PRP$	12.50	15.00	15.00	12.00	12.00	12.00	20.00
decision:NN	10.50	14.46	12.38	8.69	0.00	8.84	18.75
to:TO	20.50	17.95	20.00	20.00	20.00	20.00	10.00
go_to_go_to_war:VB	11.79	9.67	7.80	13.92	13.84	5.00	20.00
Iraq:NNP	14.34	14.84	14.84	9.84	10.50	10.17	20.50
.:.	20.50	20.00	18.74	20.00	18.75	20.00	0.00
NO_WORD	10.00	1.00	10.00	10.00	10.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -36.0073
Features matched: Adjunct.dropPosCxt: text adjunct "Iraq" of "go_to_go_to_war" dropped on aligned hyp word "go_to_war"; NullPunisher.other: Trump; NullPunisher.other: W; NullPunisher.aux: does; RootEntailment.poorlyAlignedRoot: "support" aligned badly to "criticized"; Structure.parentsMismatch: args have different parents, different relations: text "decision" <-prep_over-- "President_Bush" vs. hyp "decision" <-dobj-- "support", which aligned to text "criticized" args have different parents, different relations: text "go_to_go_to_war" <-infmod-- "decision" vs. hyp "go_to_war" <-prep_to-- "support", which aligned to text "criticized"
Hand-tuned score: -5.5500
Threshold: -11.4590

Inference ID: Cycorp-021

Txt: LEDs can last ten years, whereas an incandescent bulb typically lasts 5000 hours

Hyp: Incandescent bulbs do last longer than LEDs . (No.)

	Incandescent JJ	bulbs NNS	do VBP	last RB	longer RB	LEDs NNP	. .
LEDs:NNS	11.96	6.40	15.00	11.40	13.95	0.00	20.00
can:MD	19.96	16.69	15.67	17.12	18.95	17.12	10.00
last:VB	11.96	11.40	7.63	0.00	18.95	11.40	20.00
ten:NN	11.96	9.68	13.69	11.46	12.31	10.00	19.42
years:NNS	11.96	8.69	13.69	12.84	14.54	10.00	19.49
,:,	20.00	20.00	19.52	20.00	19.51	20.00	5.73
whereas:IN	20.00	20.00	20.00	20.00	20.00	20.00	20.00
an:DT	20.00	20.00	20.00	20.00	20.00	20.00	10.00
incandescent:NN	2.00	6.14	14.96	14.96	14.35	9.96	19.79
bulb:NN	11.96	0.50	13.66	11.40	13.95	6.40	20.00
typically:RB	11.96	14.28	19.58	9.96	5.29	14.96	19.79
lasts:VBZ	11.96	11.40	7.69	10.31	14.40	11.40	19.71
5000:JJ	9.96	11.40	11.46	11.96	11.96	11.96	20.00
hours:NNS	11.96	7.89	13.69	12.84	12.46	10.00	19.95
NO_WORD	9.00	10.00	10.00	9.00	9.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -29.2083
Features matched: RootEntailment.poorlyAlignedRoot: "do" aligned badly to "lasts"; Structure.relMismatch: text "LEDs" is nsubj of "last" while hyp "LEDs" is prep_than of "do" which aligned to text "lasts"
Hand-tuned score: -2.0000
Threshold: -11.4590

Inference ID: Cycorp-022

Txt: The "Just Picture It" workshop, sponsored by Oceanside Photo and Video, Inc. and taught by portrait photographer Gale Carlson, has received rave reviews.

Hyp: Is `` Just Picture It '' a photography workshop ? (Yes.)

	Is VBZ	`` ``	Just RB	Picture VBG	It PRP	'' ''	a DT	photography NN	workshop NN	? .
The:DT	20.00	10.00	20.00	20.00	20.00	10.00	10.00	20.00	20.00	10.00
``:``	20.00	0.00	20.00	20.00	20.00	1.06	10.00	20.00	20.00	10.00
Just:RB	19.96	20.00	0.00	19.96	15.00	20.00	20.00	14.96	14.96	20.00
Picture:VBG	9.34	20.00	19.96	0.00	15.00	20.00	20.00	15.00	14.34	20.00
It:PRP	15.00	20.00	20.00	15.00	0.00	20.00	20.00	12.00	12.00	20.00
'':''	20.00	1.06	20.00	20.00	20.00	0.00	10.00	20.00	19.89	9.33
workshop:NN	14.34	20.00	14.96	14.34	12.00	19.89	20.00	7.71	0.00	20.00
,:,	20.00	6.83	20.00	20.00	20.00	6.68	10.00	19.37	19.76	10.00
sponsored:VBN	9.34	20.00	19.96	8.95	15.00	19.76	20.00	14.25	10.75	19.75
Oceanside_Photo_and_Video_,_Inc._and:NNP	15.17	20.50	15.46	11.19	12.50	20.50	20.50	10.46	10.17	20.50
taught:VBN	7.74	19.47	19.96	8.95	15.00	20.00	20.00	14.25	10.96	20.00
portrait:NN	14.34	20.00	14.96	7.08	12.00	20.00	20.00	7.62	7.79	20.00
photographer:NN	14.34	18.38	14.96	13.95	12.00	18.09	20.00	5.00	7.78	20.00
Gale_Carlson:NNP	15.46	20.50	15.46	15.46	12.50	20.50	20.50	8.97	10.46	20.50
,:,	20.50	7.33	20.50	20.50	20.50	7.18	10.50	19.87	20.26	10.50
has:VBZ	9.34	20.00	19.96	8.69	15.00	20.00	20.00	15.00	12.52	20.00
received:VBN	10.00	20.00	19.96	10.00	15.00	20.00	20.00	15.00	14.47	20.00
rave:JJ	9.74	18.71	11.96	8.36	15.00	19.62	20.00	10.71	7.13	19.37
reviews:NNS	14.34	18.81	14.96	10.45	12.00	18.20	20.00	6.36	7.29	20.00
.:.	20.00	7.32	20.00	20.00	20.00	6.80	10.00	20.00	20.00	10.00
NO_WORD	1.00	10.00	9.00	10.00	10.00	10.00	1.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -25.0000
Features matched: Adjunct.dropPosCxt: text adjunct "sponsored" of "workshop" dropped on aligned hyp word "workshop"; NullPunisher.article: a; NullPunisher.other: ?; NullPunisher.other: Is; Quant.contract: [the,a]; Structure.argsMismatch: args have different parents but same relations: text "``" <-punct-- "workshop vs. hyp "``" <-punct-- "Picture", which aligned to text "Picture" args have different parents but same relations: text "''" <-punct-- "workshop vs. hyp "''" <-punct-- "Picture", which aligned to text "Picture" args have different parents but same relations: text "workshop" <-nsubjpass-- "sponsored vs. hyp "workshop" <-dobj-- "Picture", which aligned to text "Picture" args have different parents, different relations: text "workshop" <-nsubj-- "received" vs. hyp "workshop" <-dobj-- "Picture", which aligned to text "Picture"
Hand-tuned score: -2.6000
Threshold: -11.4590

Inference ID: Cycorp-023

Txt: The equatorial diameter of Saturn is 10% larger than its polar diameter.

Hyp: Saturn is an ellipsoid . (Yes.)

	Saturn NNP	is VBZ	an DT	ellipsoid NN	. .
The:DT	20.50	20.00	10.00	20.00	10.00
equatorial:JJ	12.46	11.96	20.00	11.96	20.00
diameter:NN	10.50	15.00	20.00	5.41	19.61
Saturn:NNP	0.00	14.84	20.50	10.50	20.50
is:VBZ	14.84	0.00	20.00	15.00	20.00
10:CD	25.00	20.50	20.50	19.19	19.16
%:NN	15.00	15.50	20.50	10.50	20.50
larger:JJR	12.46	11.96	20.00	11.96	17.97
its:PRP$	12.50	13.00	20.00	12.00	20.00
polar:JJ	12.46	11.96	20.00	11.96	20.00
diameter:NN	10.50	15.00	20.00	5.41	19.61
.:.	20.50	20.00	10.00	20.00	0.00
NO_WORD	10.00	1.00	1.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -11.4140
Features matched: Adjunct.dropPosCxt: text adjunct "polar" of "diameter" dropped on aligned hyp word "ellipsoid"; NullPunisher.aux: is; NullPunisher.article: an; RootEntailment.poorlyAlignedRoot: "ellipsoid" aligned badly to "diameter"; Structure.argsMismatch: args have different parents but same relations: text "." <-punct-- "larger vs. hyp "." <-punct-- "ellipsoid", which aligned to text "diameter" text "Saturn" is prep_of of "diameter" while hyp "Saturn" is nsubj of "ellipsoid" which aligned to text "diameter"
Hand-tuned score: -3.6500
Threshold: -11.4590

Inference ID: Cycorp-024

Txt: The equatorial diameter of Saturn is 10% larger than its polar diameter.

Hyp: Saturn is a sphere . (No.)

	Saturn NNP	is VBZ	a DT	sphere NN	. .
The:DT	20.50	20.00	10.00	20.00	10.00
equatorial:JJ	12.46	11.96	20.00	11.22	20.00
diameter:NN	10.50	15.00	20.00	5.41	19.61
Saturn:NNP	0.00	14.84	20.50	7.97	20.50
is:VBZ	14.84	0.00	20.00	14.34	20.00
10:CD	25.00	20.50	20.50	19.19	19.16
%:NN	15.00	15.50	20.50	10.08	20.50
larger:JJR	12.46	11.96	20.00	11.96	17.97
its:PRP$	12.50	13.00	20.00	12.00	20.00
polar:JJ	12.46	11.96	20.00	9.25	20.00
diameter:NN	10.50	15.00	20.00	5.41	19.61
.:.	20.50	20.00	10.00	20.00	0.00
NO_WORD	10.00	1.00	1.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -11.4140
Features matched: Adjunct.dropPosCxt: text adjunct "equatorial" of "diameter" dropped on aligned hyp word "sphere"; NullPunisher.article: a; NullPunisher.aux: is; Quant.contract: [the,a]; RootEntailment.poorlyAlignedRoot: "sphere" aligned badly to "diameter"; Structure.argsMismatch: args have different parents but same relations: text "." <-punct-- "larger vs. hyp "." <-punct-- "sphere", which aligned to text "diameter" text "Saturn" is prep_of of "diameter" while hyp "Saturn" is nsubj of "sphere" which aligned to text "diameter"
Hand-tuned score: -2.6500
Threshold: -11.4590

Inference ID: Cycorp-025

Txt: Charlotte Jones gave birth to a healthy baby boy, Johnathan Daniel, on March 14th, 2004.

Hyp: Charlotte Jones was pregnant on December 14 , 2003 . (Yes.)

	Charlotte_Jones NNS	was VBD	pregnant JJ	December NNP	14 CD	, ,	2003 CD	. .
Charlotte_Jones:NNS	5.00	13.67	12.46	14.96	24.96	25.00	24.96	20.50
gave_birth:VBD	15.46	10.00	11.96	14.42	19.42	20.50	20.46	20.00
a:DT	20.50	20.00	20.00	20.50	20.50	10.50	20.50	10.00
healthy:JJ	12.46	11.96	6.37	12.46	20.37	20.50	20.46	19.46
baby:NN	9.52	14.34	5.63	10.50	20.50	19.20	20.46	19.63
boy:NN	9.52	14.34	6.78	10.50	20.50	19.17	20.46	19.66
,:,	25.00	20.50	19.69	20.00	18.78	0.00	19.45	6.23
Johnathan_Daniel:NNP	12.99	15.17	12.46	14.34	24.34	25.00	24.96	20.50
,:,	25.00	20.50	19.69	20.00	18.78	0.00	19.45	6.23
March:NNP	13.19	11.88	12.46	4.57	17.84	20.00	19.96	20.50
14th:CD	24.96	20.46	20.46	19.96	5.00	20.00	5.00	20.45
,:,	25.00	20.50	19.69	20.00	18.78	0.00	19.45	6.23
2004:CD	24.96	20.46	20.15	19.96	4.96	19.10	0.00	20.50
.:.	20.50	20.00	19.79	20.50	19.34	6.23	20.50	0.00
NO_WORD	10.00	1.00	9.00	10.00	10.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -27.1610
Features matched: Adjunct.dropPosCxt: text adjunct "14th" of "March" dropped on aligned hyp word "December"; Date.dateHeadMismatch: December vs. March; NullPunisher.aux: was; RootEntailment.poorlyAlignedRoot: "pregnant" aligned badly to "baby"; Structure.argsMismatch: args have different parents but same relations: text "Charlotte_Jones" <-nsubj-- "gave_birth vs. hyp "Charlotte_Jones" <-nsubj-- "pregnant", which aligned to text "baby" args have different parents but same relations: text "March" <-prep_on-- "gave_birth vs. hyp "December" <-prep_on-- "pregnant", which aligned to text "baby" args have different parents but same relations: text "." <-punct-- "gave_birth vs. hyp "." <-punct-- "pregnant", which aligned to text "baby"
Hand-tuned score: -6.5500
Threshold: -11.4590

Inference ID: Cycorp-026

Txt: Keikaimalu, a wholphin (whale-dolphin hybrid), has given birth to female calf.

Hyp: Keikaimalu did gave_birth to a ruminant . (No.)

	Keikaimalu NNP	did VBD	gave_birth NN	a DT	ruminant NN	. .
Keikaimalu:NNP	0.00	15.46	10.46	20.50	10.46	20.50
,:,	20.50	19.80	20.00	10.00	20.00	5.73
a:DT	20.50	20.00	20.00	0.00	20.00	10.00
wholphin:NN	10.46	14.96	9.96	20.00	9.96	20.00
-LRB-:-LRB-	20.50	20.00	20.00	10.00	20.00	10.00
whale-dolphin:JJ	12.46	11.51	11.96	20.00	11.96	20.00
hybrid:NN	10.46	11.24	9.02	20.00	8.11	19.87
-RRB-:-RRB-	20.50	20.00	20.00	10.00	20.00	10.00
,:,	20.50	19.80	20.00	10.00	20.00	5.73
has:VBZ	15.46	7.53	13.76	20.00	14.34	20.00
given_birth:VBN	15.46	7.32	5.00	20.00	15.00	20.00
female:JJ	12.46	12.00	12.00	20.00	7.76	20.00
calf:NN	10.46	13.64	10.00	20.00	5.76	20.00
.:.	20.50	17.99	20.00	10.00	20.00	0.00
NO_WORD	10.00	10.00	10.00	1.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -21.0762
Features matched: Adjunct.dropPosCxt: text adjunct "female" of "calf" dropped on aligned hyp word "ruminant"; NullPunisher.article: a; RootEntailment.poorlyAlignedRoot: "did" aligned badly to "given_birth"
Hand-tuned score: -0.6000
Threshold: -11.4590

Inference ID: Cycorp-027

Txt: A wholphin is a whale-dolphin hybrid.

Hyp: The term does `` wholphin '' refer to a type of hybrid . (Yes.)

	The DT	term NN	does VBZ	`` ``	wholphin NN	'' ''	refer VBP	a DT	type NN	hybrid NN	. .
A:DT	10.00	20.00	20.00	10.00	20.00	10.00	20.00	0.00	20.00	20.00	10.00
wholphin:NN	20.00	9.96	14.96	20.00	0.00	20.00	14.96	20.00	9.96	9.96	20.00
is:VBZ	20.00	14.34	9.34	20.00	14.96	20.00	5.51	20.00	12.74	14.34	20.00
a:DT	10.00	20.00	20.00	10.00	20.00	10.00	20.00	0.00	20.00	20.00	10.00
whale-dolphin:JJ	20.00	11.96	11.21	20.00	7.00	19.85	11.13	20.00	11.34	9.67	20.00
hybrid:NN	20.00	5.91	13.05	20.00	9.96	20.00	14.48	20.00	6.67	0.00	19.87
.:.	10.00	19.66	20.00	7.32	20.00	6.80	19.97	10.00	17.87	19.87	0.00
NO_WORD	1.00	10.00	10.00	10.00	10.00	10.00	10.00	1.00	10.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -58.5544
Features matched: Adjunct.dropPosCxt: text adjunct "whale-dolphin" of "hybrid" dropped on aligned hyp word "hybrid"; NullPunisher.article: The; Quant.contract: [a,the]; Quant.contract: [a,a]; RootEntailment.poorlyAlignedRoot: "does" aligned badly to "is"; Structure.argsMismatch: args have different parents but same relations: text "." <-punct-- "hybrid vs. hyp "." <-punct-- "does", which aligned to text "is" args have different parents, different relations: text "is" <-cop-- "hybrid" vs. hyp "refer" <-ccomp-- "does", which aligned to text "is"
Hand-tuned score: -1.6000
Threshold: -11.4590

Inference ID: Cycorp-028

Txt: Marilyn Connors had a very difficult pregnancy and died in childbirth yesterday.

Hyp: Marilyn Connors is pregnant . (No.)

	Marilyn_Connors NNS	is VBZ	pregnant JJ	. .
Marilyn_Connors:NNS	0.00	15.17	12.46	20.50
had:VBD	14.52	7.80	11.96	20.00
a:DT	20.50	20.00	20.00	10.00
very:RB	15.46	19.96	11.96	20.00
difficult:JJ	12.46	11.96	9.96	17.00
pregnancy:NN	10.46	15.00	3.75	19.77
died:VBD	14.97	8.07	7.91	20.00
childbirth:NN	10.46	15.00	5.16	20.00
yesterday:NN	10.46	15.00	11.96	18.11
.:.	20.50	20.00	19.79	0.00
NO_WORD	10.00	1.00	9.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -8.7539
Features matched: Adjunct.dropPosCxt: text adjunct "difficult" of "pregnancy" dropped on aligned hyp word "pregnant"; NullPunisher.aux: is; Structure.argsMismatch: args have different parents but same relations: text "Marilyn_Connors" <-nsubj-- "had vs. hyp "Marilyn_Connors" <-nsubj-- "pregnant", which aligned to text "pregnancy" args have different parents but same relations: text "." <-punct-- "had vs. hyp "." <-punct-- "pregnant", which aligned to text "pregnancy"
Hand-tuned score: -1.5500
Threshold: -11.4590

Inference ID: Cycorp-029

Txt: Serena Williams is a great tennis player, perhaps one of the best ever.

Hyp: Serena Williams does play tennis well . (Yes.)

	Serena_Williams NNS	does VBZ	play NN	tennis NN	well RB	. .
Serena_Williams:NNS	5.00	14.55	10.46	10.46	14.97	20.50
is:VBZ	15.17	9.34	13.07	15.00	19.34	20.00
a:DT	20.50	20.00	20.00	20.00	20.00	10.00
great:JJ	12.46	11.01	8.17	10.79	11.96	17.92
tennis_player:NN	7.17	13.11	5.00	0.00	13.95	20.00
,:,	20.50	19.26	19.21	20.00	20.00	5.73
perhaps:RB	15.46	19.96	14.96	14.96	9.96	20.00
one:CD	24.96	20.50	17.49	20.50	19.19	20.50
the:DT	20.50	18.65	20.00	20.00	20.00	10.00
best:JJS	11.52	9.70	7.41	8.49	10.95	18.78
ever:RB	15.46	17.36	14.96	14.96	9.96	20.00
.:.	20.50	20.00	18.13	20.00	20.00	0.00
NO_WORD	10.00	10.00	10.00	10.00	9.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -36.1080
Features matched: Adjunct.addPosCxt: hyp added well[well-RB]; Adjunct.dropPosCxt: text adjunct "ever" of "tennis_player" dropped on aligned hyp word "tennis"; Modal.dontKnow: possible -> actual; NullPunisher.other: well; RootEntailment.poorlyAlignedRoot: "does" aligned badly to "tennis_player"
Hand-tuned score: -4.5000
Threshold: -11.4590

Inference ID: Cycorp-30

Txt: War and Peace is a very long novel.

Hyp: War and Peace does contain more pages than the average novel . (Yes.)

	War NNP	Peace NNP	does VBZ	contain VB	more JJR	pages NNS	the DT	average JJ	novel NN	. .
War:NNP	0.00	7.94	15.00	15.00	12.00	10.00	20.00	12.00	10.00	20.00
Peace:NNP	7.94	0.00	15.50	15.50	12.50	10.50	20.50	10.40	7.27	20.50
is:VBZ	15.00	15.50	9.34	8.33	11.34	12.34	20.00	10.33	14.34	20.00
a:DT	20.00	20.50	20.00	20.00	20.00	20.00	10.00	20.00	20.00	10.00
very:RB	14.96	15.46	19.96	19.96	11.96	14.96	20.00	11.96	14.96	20.00
long:JJ	12.00	11.19	11.33	12.00	8.25	12.00	20.00	10.00	10.69	17.94
novel:NN	10.00	7.27	13.95	13.75	10.95	6.58	20.00	12.00	0.00	19.66
.:.	20.00	20.50	20.00	18.50	20.00	20.00	10.00	19.31	19.66	0.00
NO_WORD	10.00	10.00	1.00	10.00	9.00	10.00	1.00	9.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -42.1560
Features matched: Adjunct.addPosCxt: hyp added average[average-JJ]; Adjunct.dropPosCxt: text adjunct "very" of "long" dropped on aligned hyp word "more"; NullPunisher.aux: does; NullPunisher.article: the; NullPunisher.other: average; Quant.contract: [a,the]; RootEntailment.poorlyAlignedRoot: "contain" aligned badly to "is"; Structure.argsMismatch: args have different parents but same relations: text "War" <-nsubj-- "novel vs. hyp "War" <-nsubj-- "contain", which aligned to text "is" args have different parents but same relations: text "." <-punct-- "novel vs. hyp "." <-punct-- "contain", which aligned to text "is"
Hand-tuned score: -4.6500
Threshold: -11.4590

Inference ID: Cycorp-031

Txt: Ambassador Richard C. Holbrooke will visit Paris, France from Saturday, April 22 until Tuesday, April 25.

Hyp: Ambassador Holbrooke will be in Paris on April 24th . (Yes.)

	Ambassador NNP	Holbrooke NNP	will MD	be VB	Paris NNP	April NNP	24th JJ	. .
Ambassador:NNP	0.00	9.29	20.00	14.34	9.84	10.50	12.46	20.00
Richard_C._Holbrooke:NNP	10.46	0.00	20.46	15.46	14.96	14.96	16.96	20.50
will:MD	20.00	20.46	0.00	20.00	20.50	19.19	20.46	10.00
visit:VB	15.00	15.46	20.00	7.74	13.63	15.50	11.05	20.00
Paris:NNP	9.84	14.96	20.50	14.84	0.00	15.00	16.96	20.50
,:,	20.50	25.00	10.50	20.50	25.00	20.00	20.00	6.23
France:NNP	8.55	14.96	20.50	14.84	2.00	15.00	16.96	20.50
Saturday:NNP	10.50	14.96	19.19	15.50	15.00	7.23	11.96	20.50
,:,	20.50	25.00	10.50	20.50	25.00	20.00	20.00	6.23
April:NNP	10.50	14.96	19.19	15.50	15.00	0.00	11.96	20.50
22:CD	20.50	24.96	19.19	20.50	25.00	17.84	19.56	19.32
Tuesday:NNP	10.50	14.96	19.19	15.50	15.00	7.23	11.96	20.50
,:,	20.50	25.00	10.50	20.50	25.00	20.00	20.00	6.23
April:NNP	10.50	14.96	19.19	15.50	15.00	0.00	11.96	20.50
25:CD	20.50	24.96	19.19	20.50	25.00	17.84	19.96	19.52
.:.	20.00	20.50	10.00	20.00	20.50	20.50	20.50	0.00
NO_WORD	10.00	10.00	10.00	10.00	10.00	10.00	9.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -20.7439
Features matched: Adjunct.dropPosCxt: text adjunct "22" of "April" dropped on aligned hyp word "April"; Date.matchDatesByGraph: hyp/txt matching, by graph: April and children; Date.hypDateIns: hypothesis date insertion: 24th; NullPunisher.other: 24th; RootEntailment.poorlyAlignedRoot: "be" aligned badly to "visit"; Structure.relMismatch: text "Paris" is dobj of "visit" while hyp "Paris" is prep_in of "be" which aligned to text "visit"
Hand-tuned score: -3.5000
Threshold: -11.4590

Inference ID: Cycorp-032

Txt: The Island Nut Sampler includes an 8 ounce box of milk chocolate covered macadamia nuts and an 8 ounce box of white chocolate covered macadamia nuts.

Hyp: It does include some dark-chocolate covered macadamia nuts . (No.)

	It PRP	does VBZ	include VB	some DT	dark-chocolate JJ	covered JJ	macadamia NN	nuts NNS	. .
The:DT	20.00	20.00	20.00	10.00	20.00	20.00	20.00	20.00	10.00
Island:NNP	12.50	14.45	15.50	20.50	12.46	11.45	9.45	9.45	20.50
Nut:NNP	12.50	13.61	15.50	20.50	12.46	8.96	8.61	0.81	20.50
Sampler:NNP	12.50	13.61	15.50	20.50	12.46	10.53	8.61	8.53	20.50
includes:VBZ	15.00	9.55	0.50	20.00	11.96	7.64	14.87	15.00	19.56
an:DT	20.00	17.89	20.00	10.00	20.00	20.00	20.00	20.00	10.00
8:CD	20.50	20.50	20.50	20.50	19.13	19.42	20.50	18.34	19.96
ounce:NN	12.00	10.17	15.00	20.00	11.66	10.95	8.11	7.84	19.86
box:NN	12.00	13.08	12.42	20.00	8.60	6.85	4.40	7.81	19.82
milk_chocolate:NN	12.00	14.34	12.87	20.00	7.00	10.81	8.53	8.44	20.00
covered:VBN	15.00	8.49	5.64	20.00	8.32	0.00	13.95	11.46	19.33
macadamia:NN	12.00	13.11	13.91	20.00	7.79	10.95	0.00	5.49	19.94
nuts:NNS	12.00	13.11	15.00	20.00	6.73	8.46	5.49	0.00	19.80
an:DT	20.00	17.89	20.00	10.00	20.00	20.00	20.00	20.00	10.00
8:CD	20.50	20.50	20.50	20.50	19.13	19.42	20.50	18.34	19.96
ounce:NN	12.00	10.17	15.00	20.00	11.66	10.95	8.11	7.84	19.86
box:NN	12.00	13.08	12.42	20.00	8.60	6.85	4.40	7.81	19.82
white_chocolate:NN	12.00	14.05	13.81	20.00	7.00	9.89	9.05	8.44	20.00
covered:VBD	15.00	8.49	5.64	20.00	8.32	0.00	13.95	11.46	19.33
macadamia:NN	12.00	13.11	13.91	20.00	7.79	10.95	0.00	5.49	19.94
nuts:NNS	12.00	13.11	15.00	20.00	6.73	8.46	5.49	0.00	19.80
.:.	20.00	20.00	19.86	10.00	19.36	19.33	19.94	19.80	0.00
NO_WORD	10.00	1.00	10.00	10.00	9.00	9.00	10.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -34.2319
Features matched: NullPunisher.other: It; NullPunisher.other: some; NullPunisher.aux: does; Structure.argsMismatch: args have different parents but same relations: text "nuts" <-dobj-- "covered vs. hyp "nuts" <-dobj-- "include", which aligned to text "includes"
Hand-tuned score: -4.0500
Threshold: -11.4590

Inference ID: Cycorp-033

Txt: A quarter of all cellphones have built-in cameras.

Hyp: Do most cell_phones have a lens . (No.)

	Do VB	most JJS	cell_phones NNS	have VB	a DT	lens NN	. .
A:DT	20.00	20.00	20.00	20.00	0.00	20.00	10.00
quarter:NN	10.67	11.96	9.02	12.61	20.00	7.90	19.74
all:DT	20.00	20.00	20.00	20.00	10.00	20.00	10.00
cellphones:NNS	15.00	11.96	1.90	13.95	20.00	3.53	20.00
have:VBP	7.32	11.96	12.80	0.00	20.00	13.95	20.00
built-in:JJ	11.96	9.96	11.96	11.96	20.00	11.96	20.00
cameras:NNS	15.00	11.96	6.77	13.95	20.00	3.53	19.47
.:.	20.00	20.00	20.00	20.00	10.00	20.00	0.00
NO_WORD	10.00	9.00	10.00	10.00	1.00	10.00	10.00

Response: yes (INCORRECT)
Justification:
Alignment score: -26.7570
Features matched: Adjunct.addPosCxt: hyp added most[most-JJS]; Adjunct.dropPosCxt: text adjunct "built-in" of "cameras" dropped on aligned hyp word "lens"; Polarity.hypNegMarker: "most": JJS; NullPunisher.article: a; NullPunisher.other: most; Quant.contract: [all,most]; RootEntailment.poorlyAlignedRoot: "Do" aligned badly to "have"
Hand-tuned score: -1.6000
Threshold: -11.4590

Inference ID: Cycorp-034

Txt: Three quarters of all cellphones have a built-in camera.

Hyp: Do most cell_phones have a lens . (Yes.)

	Do VB	most JJS	cell_phones NNS	have VB	a DT	lens NN	. .
Three:CD	19.19	20.46	19.84	20.50	20.50	20.50	20.50
quarters:NNS	15.00	11.96	8.53	13.95	20.00	8.03	19.46
all:DT	20.00	20.00	20.00	20.00	10.00	20.00	10.00
cellphones:NNS	15.00	11.96	1.90	13.95	20.00	3.53	20.00
have:VBP	7.32	11.96	12.80	0.00	20.00	13.95	20.00
a:DT	20.00	20.00	20.00	20.00	0.00	20.00	10.00
built-in:JJ	11.96	9.96	11.96	11.96	20.00	11.96	20.00
camera:NN	15.00	11.96	6.77	13.95	20.00	3.53	18.65
.:.	20.00	20.00	20.00	20.00	10.00	20.00	0.00
NO_WORD	10.00	9.00	10.00	10.00	1.00	10.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -25.7570
Features matched: Adjunct.addPosCxt: hyp added most[most-JJS]; Adjunct.dropPosCxt: text adjunct "built-in" of "camera" dropped on aligned hyp word "lens"; Polarity.hypNegMarker: "most": JJS; NullPunisher.other: most; Quant.contract: [all,most]; Quant.contract: [a,a]; RootEntailment.poorlyAlignedRoot: "Do" aligned badly to "have"
Hand-tuned score: -0.5000
Threshold: -11.4590

Inference ID: Cycorp-035

Txt: Marilyn Connors had a very difficult pregnancy and died in childbirth yesterday.

Hyp: Marilyn Connors was pregnant . (Yes.)

	Marilyn_Connors NNS	was VBD	pregnant JJ	. .
Marilyn_Connors:NNS	0.00	15.17	12.46	20.50
had:VBD	14.52	9.34	11.96	20.00
a:DT	20.50	20.00	20.00	10.00
very:RB	15.46	19.96	11.96	20.00
difficult:JJ	12.46	11.96	9.96	17.00
pregnancy:NN	10.46	15.00	3.75	19.77
died:VBD	14.97	9.34	7.91	20.00
childbirth:NN	10.46	15.00	5.16	20.00
yesterday:NN	10.46	15.00	11.96	18.11
.:.	20.50	20.00	19.79	0.00
NO_WORD	10.00	1.00	9.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -8.7539
Features matched: Adjunct.dropPosCxt: text adjunct "difficult" of "pregnancy" dropped on aligned hyp word "pregnant"; NullPunisher.aux: was; Structure.argsMismatch: args have different parents but same relations: text "Marilyn_Connors" <-nsubj-- "had vs. hyp "Marilyn_Connors" <-nsubj-- "pregnant", which aligned to text "pregnancy" args have different parents but same relations: text "." <-punct-- "had vs. hyp "." <-punct-- "pregnant", which aligned to text "pregnancy"
Hand-tuned score: -1.5500
Threshold: -11.4590

Inference ID: Cycorp-036

Txt: Marilyn Connors had a very difficult pregnancy and died in childbirth yesterday.

Hyp: Marilyn Connors is dead . (Yes.)

	Marilyn_Connors NNS	is VBZ	dead JJ	. .
Marilyn_Connors:NNS	0.00	15.17	12.46	20.50
had:VBD	14.52	7.80	12.00	20.00
a:DT	20.50	20.00	20.00	10.00
very:RB	15.46	19.96	11.96	20.00
difficult:JJ	12.46	11.96	9.96	17.00
pregnancy:NN	10.46	15.00	9.39	19.77
died:VBD	14.97	8.07	5.77	20.00
childbirth:NN	10.46	15.00	8.86	20.00
yesterday:NN	10.46	15.00	9.84	18.11
.:.	20.50	20.00	18.65	0.00
NO_WORD	10.00	1.00	9.00	10.00

Response: yes (CORRECT)
Justification:
Alignment score: -10.7745
Features matched: Adjunct.dropPosCxt: text adjunct "yesterday" of "died" dropped on aligned hyp word "dead"; NullPunisher.aux: is; RootEntailment.poorlyAlignedRoot: "dead" aligned badly to "died"; Structure.argsMismatch: args have different parents but same relations: text "Marilyn_Connors" <-nsubj-- "had vs. hyp "Marilyn_Connors" <-nsubj-- "dead", which aligned to text "died" args have different parents but same relations: text "." <-punct-- "had vs. hyp "." <-punct-- "dead", which aligned to text "died"
Hand-tuned score: -3.5500
Threshold: -11.4590

Word similarity table built on Wed Jul 05 15:16:09 PDT 2006 using command:

java edu.stanford.nlp.rte.WordSimilarityGenerator -info /u/nlp/rte/data/byformat/align/stochastic/cycorp_dev.pipeline.align.xml -output /u/nlp/rte/data/byformat/wordsim/stochastic/cycorp_dev.pipeline.wordsim.html -lex.BasicWN off