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: null
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
WNHyponymy: WNHyponymyLexicalResource
WNSynonymy: WNSynonymyLexicalResource
Nominalization: NominalizationLexicalResource
MTParaphrase: null
DIRTParaphrase: null
WordWalk: null
ThesaurusAntonymy: null

Inference ID: 1

Txt: The sale was made to pay Yukos' US$ 27.5 billion tax bill, Yuganskneftegaz was originally sold for US$ 9.4 billion to a little known company Baikalfinansgroup which was later bought by the Russian state-owned oil company Rosneft .

Hyp: Baikalfinansgroup was sold to Rosneft. (yes)

	Baikalfinansgroup NNP	was VBD	sold VBN	Rosneft NNP
The:DT	3.45	2.55	2.55	3.45
sale:NN	2.65	1.59	0.98	2.65
was:VBD	2.76	1.63	1.67	2.76
made:VBN	2.76	1.58	1.11	2.76
to:TO	3.45	2.55	2.55	3.45
pay:VB	2.76	1.43	0.82	2.76
Yukos:NNP	1.90	2.46	2.46	2.39
US$_27.5_billion:CD	2.58	2.90	2.52	2.60
tax_bill:NN	2.65	1.71	1.71	2.65
Yuganskneftegaz:NNP	1.78	2.46	2.46	2.32
was:VBD	2.76	1.63	1.67	2.76
originally:RB	3.21	1.66	0.73	3.20
sold:VBN	2.76	1.67	1.63	2.76
US$_9.4_billion:CD	2.60	2.90	2.52	2.60
a:DT	3.45	2.55	2.55	3.45
little:RB	3.25	1.66	1.41	3.25
known:VBN	2.76	1.64	0.84	2.76
company:NN	2.65	1.71	1.38	2.53
Baikalfinansgroup:NN	2.89	2.46	2.46	2.39
which:WDT	3.45	2.55	2.55	3.45
was:VBD	2.76	1.63	1.67	2.76
later:RB	3.25	1.66	0.85	3.25
bought:VBN	2.76	1.69	2.09	2.57
the:DT	3.45	2.55	2.55	3.45
Russian:JJ	2.38	3.11	3.11	2.14
state-owned:JJ	2.64	2.36	1.48	2.64
oil_company:NN	2.65	1.71	1.38	2.65
Rosneft:NN	2.39	2.46	2.46	2.89
NO_WORD	0.40	2.48	0.23	1.29

Inference ID: 2

Txt: The sale was made to pay Yukos' US$ 27.5 billion tax bill, Yuganskneftegaz was originally sold for US$9.4 billion to a little known company Baikalfinansgroup which was later bought by the Russian state-owned oil company Rosneft .

Hyp: Yuganskneftegaz cost US$ 27.5 billion. (don't know)

	Yuganskneftegaz JJ	cost NN	US$_27.5_billion CD
The:DT	2.97	2.71	3.11
sale:NN	3.21	1.61	2.34
was:VBD	3.25	1.89	3.01
made:VBN	3.25	2.01	2.92
to:TO	2.97	2.71	3.11
pay:VB	3.25	0.55	3.01
Yukos:NNP	2.95	2.56	2.54
US$_27.5_billion:CD	2.64	2.78	2.71
tax_bill:NN	3.21	1.55	2.35
Yuganskneftegaz:NNP	1.25	2.65	2.54
was:VBD	3.25	1.89	3.01
originally:RB	2.92	2.23	2.59
sold:VBN	3.25	1.80	2.62
US$_9.4_billion:CD	2.64	2.78	3.26
a:DT	2.97	2.71	3.11
little:RB	2.92	2.50	2.97
known:VBN	3.25	1.92	2.69
company:NN	3.21	1.85	2.40
Baikalfinansgroup:NN	2.90	2.65	2.52
which:WDT	2.97	2.71	3.11
was:VBD	3.25	1.89	3.01
later:RB	2.92	2.50	2.80
bought:VBN	3.25	1.85	2.47
the:DT	2.97	2.71	3.11
Russian:JJ	2.35	2.64	2.77
state-owned:JJ	2.61	1.89	3.03
oil_company:NN	3.21	1.82	2.40
Rosneft:NN	2.88	2.42	2.54
NO_WORD	0.29	0.11	1.44

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.12 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.68 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 1.64 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 1.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 1
0.10 0.33 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 1/3.0 = 0.33
-4.00 0.27 NullPunisher.other : cost
-6.00 1.00 Numeric.mismatch : MONEY mismatch: '$2.75E10' vs '$9.4E9'
-2.00 1.00 RootEntailment.unalignedRoot : "cost" not aligned to anything
Hand-tuned score (dot product of above): -2.9355
Threshold: 0.1863

Inference ID: 3

Txt: Loraine besides participating in Broadway's Dreamgirls, also participated in the Off-Broadway production of "Does A Tiger Have A Necktie". In 1999, Loraine went to London, United Kingdom. There she participated in the production of "RENT" where she was cast as "Mimi" the understudy.

Hyp: "Does A Tiger Have A Necktie" was produced in London. (don't know)

	Does NNP	A_Tiger_Have_A_Necktie NNP	was VBD	produced VBN	London NNP
Loraine:NNP	2.65	1.84	2.46	2.40	2.07
participating:VBG	2.01	2.76	1.58	1.08	2.76
Broadway:NNP	2.66	2.40	2.46	2.25	1.73
Dreamgirls:NNS	2.65	1.84	2.46	2.46	2.39
also:RB	2.50	3.25	1.54	1.66	3.25
participated:VBN	2.01	2.76	1.58	0.82	2.76
the:DT	2.59	3.45	2.55	2.55	3.45
Off-Broadway:JJ	1.89	2.64	2.36	2.36	2.64
production:NN	1.92	2.67	1.71	3.50	2.59
Does:NNP	4.06	2.66	1.59	1.71	2.69
A_Tiger_Have_A_Necktie:NNP	2.66	4.06	2.46	2.46	2.43
1999:CD	2.86	2.60	2.90	2.90	2.60
Loraine:NNP	2.65	1.84	2.46	2.40	2.07
went:VBD	2.01	2.76	1.64	1.70	2.76
London:NNP	2.69	2.43	2.46	2.46	4.06
United_Kingdom:NNP	2.69	2.43	2.46	2.46	1.67
There:RB	2.41	3.25	1.66	1.66	3.25
she:PRP	2.65	1.84	2.46	2.40	2.07
participated:VBD	2.01	2.76	1.58	0.82	2.76
the:DT	2.59	3.45	2.55	2.55	3.45
production:NN	1.92	2.67	1.71	3.50	2.59
RENT:NNP	1.90	2.65	1.71	1.71	2.67
where:WRB	2.61	3.45	2.55	2.55	3.45
she:PRP	2.68	3.54	2.24	2.24	3.54
was:VBD	1.89	2.76	1.63	1.74	2.76
cast:VBN	2.01	2.76	1.24	0.52	2.76
Mimi:VBG	2.76	1.95	2.18	2.18	2.50
the:DT	2.59	3.45	2.55	2.55	3.45
understudy:NN	1.90	2.65	1.71	1.30	2.59
NO_WORD	0.30	0.40	2.48	0.23	0.38

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.65 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.13 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 4.84 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.60 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 3/5.0 = 0.60
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Off-Broadway" modifying "production" is dropped on aligned hypothesis word "produced"
0.50 1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.5559
Threshold: 0.1863

Inference ID: 4

Txt: "The Extra Girl" (1923) is a story of a small-town girl, Sue Graham (played by Mabel Normand) who comes to Hollywood to be in the pictures. This Mabel Normand vehicle, produced by Mack Sennett, followed earlier films about the film industry and also paved the way for later films about Hollywood, such as King Vidor's "Show People" (1928).

Hyp: "The Extra Girl" was produced by Sennett. (yes)

	The DT	Extra_Girl NNP	was VBD	produced VBN	Sennett NNP
The:DT	1.26	3.45	2.55	2.55	3.45
Extra_Girl:NNP	2.90	4.06	2.46	2.46	1.84
(1923):NN	2.16	2.65	1.71	1.71	2.65
is:VBZ	2.16	2.76	1.62	1.74	2.76
a:DT	3.69	3.45	2.55	2.55	3.45
story:NN	2.16	2.65	1.71	1.63	2.65
a:DT	3.69	3.45	2.55	2.55	3.45
small-town:JJ	2.16	2.64	2.36	2.36	2.59
girl:NN	2.90	1.65	2.46	2.35	1.84
Sue_Graham:NNP	2.90	1.75	2.46	2.46	1.85
played:VBN	2.16	2.76	1.69	0.56	2.70
Mabel_Normand:NNP	2.90	1.80	2.46	2.46	1.90
who:WP	1.34	3.54	1.96	2.24	3.54
comes:VBZ	2.16	2.76	1.68	1.08	2.76
Hollywood:NNP	2.90	2.39	2.46	2.41	2.39
to:TO	3.52	3.45	2.55	2.55	3.45
be:VB	1.99	2.76	2.28	1.52	2.76
the:DT	2.93	3.45	2.55	2.55	3.45
pictures:NNS	2.16	2.57	1.71	0.21	2.65
This:DT	3.33	3.45	2.42	2.55	3.45
Mabel_Normand:NNP	2.90	1.80	2.46	2.46	1.90
vehicle:NN	2.16	2.58	1.71	1.34	2.53
produced:VBN	2.16	2.76	1.74	1.63	2.76
Mack_Sennett:NNP	2.90	1.84	2.46	2.46	2.31
followed:VBD	2.16	2.76	1.64	0.43	2.76
earlier:JJR	2.16	2.49	2.36	2.09	2.64
films:NNS	2.16	2.58	1.71	0.23	2.65
the:DT	2.93	3.45	2.55	2.55	3.45
film_industry:NN	2.16	2.64	1.71	1.22	2.65
also:RB	1.81	3.25	1.54	1.66	3.25
paved:VBD	2.16	2.76	1.53	1.31	2.76
the:DT	2.93	3.45	2.55	2.55	3.45
way:NN	2.16	2.66	1.15	1.35	2.65
later:JJ	2.16	2.64	2.36	2.03	2.64
films:NNS	2.16	2.58	1.71	0.23	2.65
Hollywood:NNP	2.90	2.39	2.46	2.41	2.39
King:NNP	2.16	2.52	1.71	1.71	2.65
Vidor:NNP	2.90	1.90	2.46	2.46	1.84
Show:NNP	2.03	2.66	1.71	1.71	2.65
People:NNS	2.16	2.67	1.71	1.47	2.58
(1928):NN	2.16	2.65	1.71	0.75	2.65
NO_WORD	2.31	0.40	2.48	0.23	0.96

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.26 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.44 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 2.90 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.60 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 3/5.0 = 0.60
Hand-tuned score (dot product of above): 0.6989
Threshold: 0.1863

Inference ID: 5

Txt: A bus collision with a truck in Uganda has resulted in at least 30 fatalities and has left a further 21 injured.

Hyp: 30 die in a bus collision in Uganda. (yes)

	30 CD	die VBP	a DT	bus NN	collision NN	Uganda NNP
A:DT	3.11	2.55	2.93	2.71	2.71	3.45
bus:NN	2.80	1.66	2.16	4.06	1.91	2.65
collision:NN	2.67	1.00	2.16	1.91	4.06	2.65
a:DT	3.11	2.55	1.26	2.71	2.71	3.45
truck:NN	2.75	1.56	2.16	0.04	1.27	2.65
Uganda:NNP	2.54	2.46	2.90	2.65	2.65	4.06
has:VBZ	3.01	1.63	2.16	1.73	2.01	2.76
resulted:VBN	2.79	1.57	2.16	2.01	1.58	2.76
at:IN	2.90	2.44	2.08	2.77	2.77	3.52
least:JJS	3.03	2.36	2.16	1.89	1.89	2.56
30:CD	2.71	2.46	2.90	2.86	2.73	2.60
fatalities:NNS	2.56	0.06	2.16	1.91	0.61	2.65
has:VBZ	3.01	1.63	2.16	1.73	2.01	2.76
left:VBN	3.01	1.58	2.16	1.38	1.04	2.76
a:DT	3.11	2.55	1.26	2.71	2.71	3.45
further:JJ	3.03	2.36	2.16	1.89	1.89	2.58
21:CD	0.39	2.90	2.90	2.82	2.54	2.60
injured:VBN	2.87	0.61	2.16	1.54	0.94	2.76
NO_WORD	1.78	0.23	2.31	0.30	0.38	0.38

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.43 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.27 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.86 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.33 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/6.0 = 0.33
-4.00 0.49 NullPunisher.other : die
-2.00 1.00 RootEntailment.unalignedRoot : "die" not aligned to anything
Hand-tuned score (dot product of above): 0.0799
Threshold: 0.1863

Inference ID: 6

Txt: Take consumer products giant Procter and Gamble. Even with a $1.8 billion Research and Development budget, it still manages 500 active partnerships each year, many of them with small companies.

Hyp: 500 small companies are partners of Procter and Gamble. (don't know)

	500 CD	small JJ	companies NNS	are VBP	partners NNS	Procter NNP	Gamble NNP
Take:VB	3.01	2.41	2.01	1.00	2.01	2.76	2.59
consumer:NN	2.62	2.40	1.34	1.71	1.68	2.48	2.73
products:NNS	2.80	2.12	1.34	1.71	1.49	2.26	2.74
giant:NN	2.80	1.72	1.43	1.71	1.05	2.65	2.61
Procter:NNP	2.54	3.21	2.65	2.46	2.37	4.06	1.84
Gamble:NNP	2.54	2.98	2.66	2.36	2.59	1.84	4.06
Even:RB	2.97	2.17	2.50	1.54	2.50	3.25	3.25
with:IN	2.90	2.16	2.77	2.44	2.77	3.52	3.52
a:DT	3.11	2.22	2.71	2.55	2.71	3.45	3.45
$_1.8_billion:CD	1.11	2.90	2.75	2.90	2.86	2.60	2.60
Research_and_Development:JJ	2.77	2.61	2.64	3.11	2.64	2.38	2.38
budget:NN	2.42	2.46	1.96	1.71	1.95	2.58	2.67
it:PRP	2.90	2.60	2.80	2.24	2.80	3.54	3.54
still:RB	2.97	1.66	2.50	1.66	2.50	3.25	3.17
manages:VBZ	2.74	2.22	1.46	1.61	1.17	2.64	2.57
500:CD	2.71	2.40	2.50	2.90	2.83	2.60	2.60
active:JJ	2.89	1.45	1.72	2.27	1.49	2.58	2.50
partnerships:NNS	2.80	1.39	0.45	1.71	2.64	2.61	2.66
each:DT	3.11	2.13	2.71	2.42	2.71	3.45	3.45
year:NN	1.81	3.10	2.74	2.34	2.73	2.39	2.45
many:JJ	3.03	1.58	1.83	2.24	1.89	2.64	2.64
them:PRP	2.90	2.60	2.80	2.12	2.80	3.54	3.54
small:JJ	2.53	3.30	0.91	2.36	1.26	2.64	2.41
companies:NNS	2.44	1.48	4.06	1.71	1.04	2.65	2.66
NO_WORD	1.44	0.29	0.61	2.58	0.11	0.05	0.05

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.49 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.23 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 4.19 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.29 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/7.0 = 0.29
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "active" modifying "partnerships" is dropped on aligned hypothesis word "partners"
1.00 1.00 Factive.positiveStatement : Valid pattern: "manages" X entails X
-4.00 0.02 NullPunisher.other : are
Hand-tuned score (dot product of above): 1.0724
Threshold: 0.1863

Inference ID: 7

Txt: After his release, the clean-shaven Magdy el-Nashar told reporters outside his home that he had nothing to do with the July 7 transit attacks, which killed 52 people and the four bombers.

Hyp: 52 people and four bombers were killed on July 7. (yes)

	52 CD	people NNS	four CD	bombers NNS	were VBD	killed VBN	July_7 CD
his:PRP$	2.90	2.80	2.90	2.80	2.24	2.24	2.90
release:NN	2.80	1.78	2.80	1.64	1.64	1.52	2.65
the:DT	3.11	2.71	3.11	2.71	2.42	2.55	3.11
clean-shaven:JJ	2.91	1.89	3.03	1.89	2.36	1.78	3.03
Magdy_el-Nashar:NN	2.54	2.69	2.54	2.65	2.46	2.46	2.54
told:VBD	3.01	1.59	2.80	2.01	1.76	0.86	3.01
reporters:NNS	2.80	1.63	2.80	1.29	1.65	1.60	2.80
his:PRP$	2.90	2.80	2.90	2.80	2.24	2.24	2.90
home:NN	2.80	1.32	2.59	1.62	1.50	1.23	2.46
that:IN	2.90	2.77	2.90	2.77	2.44	2.44	2.90
he:PRP	2.54	2.69	2.54	2.65	2.46	2.46	2.54
had:VBD	3.01	2.01	3.01	2.01	1.50	1.61	3.01
nothing:NN	2.80	1.82	2.80	1.79	1.71	1.68	2.80
to:TO	3.11	2.71	3.11	2.71	2.55	2.55	3.11
do:VB	2.98	0.82	3.01	1.72	1.46	1.45	3.01
the:DT	3.11	2.71	3.11	2.71	2.42	2.55	3.11
July_7:CD	0.39	2.86	1.06	2.86	2.90	2.76	2.71
transit:NN	2.72	1.50	2.80	0.92	1.71	1.19	2.80
attacks:NNS	2.80	1.59	2.68	0.51	1.71	0.49	2.80
which:WDT	3.11	2.71	3.11	2.71	2.45	2.55	3.11
killed:VBD	3.01	0.90	2.79	0.91	1.35	2.65	2.87
52:CD	2.71	2.86	0.34	2.86	2.90	2.90	0.39
people:NNS	2.80	4.06	2.80	1.43	1.54	0.60	2.80
the:DT	3.11	2.71	3.11	2.71	2.42	2.55	3.11
four:CD	0.34	2.86	2.71	2.74	2.90	2.69	1.06
bombers:NNS	2.80	1.43	2.68	4.06	1.64	0.57	2.80
NO_WORD	1.44	0.40	1.44	0.54	2.48	0.23	0.96

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.55 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.19 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 4.51 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 4.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 4
0.10 0.43 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 3/7.0 = 0.43
1.00 1.00 Date.matchDatesByGraph : hyp/txt matching, by graph: July_7 and children
-4.00 0.05 NullPunisher.other : were
Hand-tuned score (dot product of above): 1.0653
Threshold: 0.1863

Inference ID: 8

Txt: Mrs. Bush's approval ratings have remained very high, above 80%, even as her husband's have recently dropped below 50%.

Hyp: 80% approve of Mr. Bush. (don't know)

	80_% CD	approve VBP	Mr. NNP	Bush NNP
Mrs.:NNP	2.80	1.71	0.25	1.75
Bush:NNP	2.80	1.71	2.01	4.06
approval:NN	2.80	2.79	1.96	1.96
ratings:NNS	2.80	1.53	1.96	1.97
have:VBP	3.01	1.11	2.01	2.01
remained:VBN	2.82	1.57	2.01	2.01
very:RB	2.97	1.66	2.38	2.50
high:JJ	2.94	2.36	1.89	1.68
above:IN	2.90	2.01	2.77	2.68
80_%:CD	2.71	2.90	2.86	2.86
even:RB	2.97	1.66	2.50	2.50
as:IN	2.90	2.44	2.77	2.77
her:PRP$	2.90	2.24	2.80	2.80
husband:NN	2.79	1.67	1.96	1.54
's:VBZ	2.97	1.04	2.01	2.01
have:VB	3.01	1.11	2.01	2.01
recently:RB	2.97	1.66	2.50	2.50
dropped:VBN	3.01	1.21	2.01	2.01
50_%:CD	4.45	2.90	2.86	2.86
NO_WORD	1.78	0.23	0.30	0.05

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.44 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.27 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.73 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 4.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 4
0.10 0.75 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 3/4.0 = 0.75
-6.00 1.00 Numeric.mismatch : PERCENT mismatch: '%80.0' vs '<%50.0'
Hand-tuned score (dot product of above): -0.4680
Threshold: 0.1863

Inference ID: 9

Txt: Recent Dakosaurus research comes from a complete skull found in Argentina in 1996, studied by Diego Pol of Ohio State University, Zulma Gasparini of Argentinas National University of La Plata, and their colleagues.

Hyp: A complete Dakosaurus was discovered by Diego Pol. (don't know)

	A DT	complete JJ	Dakosaurus NNS	was VBD	discovered VBN	Diego_Pol NNP
Recent:JJ	2.16	1.86	2.64	2.36	2.36	2.64
Dakosaurus:NNP	2.90	3.21	2.89	2.46	2.29	2.35
research:NN	2.16	2.46	2.56	1.71	1.11	2.68
comes:VBZ	2.16	1.45	2.76	1.68	1.51	2.76
a:DT	2.93	2.22	3.45	2.55	2.55	3.45
complete:JJ	2.16	3.30	2.64	2.36	1.67	2.64
skull:NN	2.16	2.46	2.65	1.71	0.92	2.61
found:VBN	2.16	2.50	2.76	1.63	0.10	2.76
Argentina:NNP	2.90	3.21	1.86	2.46	2.46	2.35
1996:CD	2.90	2.90	2.60	2.90	2.90	2.60
studied:VBN	2.16	2.34	2.76	1.69	0.34	2.76
Diego_Pol_of_Ohio_State_University:NNP	2.90	3.21	2.39	2.46	2.46	1.91
Zulma_Gasparini:NNP	2.90	3.21	2.36	2.46	2.46	1.84
Argentinas_National_University_of_La_Plata:NNP	2.90	3.21	2.39	2.46	2.46	1.86
their:PRP$	1.63	2.60	3.54	2.24	2.24	3.54
colleagues:NNS	2.16	1.91	2.48	1.71	0.37	2.53
NO_WORD	2.31	0.29	0.40	2.48	0.23	0.96

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.21 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.51 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 2.62 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.33 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/6.0 = 0.33
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "colleagues" modifying "found" is dropped on aligned hypothesis word "discovered"
-4.00 0.02 NullPunisher.other : was
Hand-tuned score (dot product of above): 0.5952
Threshold: 0.1863

Inference ID: 10

Txt: On May 17, 2005, the National Assembly of Kuwait passed, by a majority of 35 to 23 (with 1 abstention), an amendment to its electoral law that would allow women to vote and to stand as parliamentary candidates.

Hyp: A pro-women amendment was rejected by the National Assembly of Kuwait. (don't know)

	A DT	pro-women JJ	amendment NN	was VBD	rejected VBN	the DT	National_Assembly_of_Kuwait NNP
May_17_,_2005:CD	2.90	2.86	2.80	2.90	2.81	2.90	2.60
the:DT	3.69	2.22	2.71	2.55	2.55	1.26	3.45
National_Assembly_of_Kuwait:NNP	2.90	3.21	2.69	2.46	2.46	2.90	4.06
passed:VBD	2.16	2.45	0.40	1.61	2.84	2.16	2.76
a:DT	2.93	2.22	2.71	2.55	2.55	3.69	3.45
majority:NN	2.16	1.99	0.42	1.71	0.58	2.16	2.70
35:CD	2.90	2.65	2.86	2.90	2.90	2.90	2.60
to:TO	3.69	2.22	2.71	2.55	2.55	3.52	3.45
23:CD	2.90	2.76	2.82	2.90	2.86	2.90	2.60
with:IN	2.36	2.16	2.77	2.32	2.44	2.24	3.52
1:CD	2.90	2.88	2.65	2.90	2.67	2.90	2.60
abstention:NN	2.16	2.10	0.94	1.71	0.95	2.16	2.66
an:DT	4.38	2.22	2.71	2.38	2.55	3.69	3.45
amendment:NN	2.16	2.18	4.06	1.71	0.31	2.16	2.69
its:PRP$	1.63	2.60	2.80	1.96	2.24	1.63	3.54
electoral:JJ	2.16	1.77	1.46	2.36	2.11	2.16	2.64
law:NN	2.16	2.46	0.51	1.43	0.99	2.16	2.71
that:WDT	3.69	2.22	2.71	2.42	2.55	3.33	3.45
would:MD	3.69	2.22	2.71	2.55	2.55	3.69	3.45
allow:VB	2.16	2.50	1.03	1.64	0.87	2.16	2.76
women:NNS	2.16	2.81	1.72	1.71	1.61	2.16	2.53
to:TO	3.69	2.22	2.71	2.55	2.55	3.52	3.45
vote:VB	2.16	2.05	0.73	1.60	0.45	2.03	2.76
to:TO	3.69	2.22	2.71	2.55	2.55	3.52	3.45
stand:VB	2.16	1.95	1.45	1.40	0.92	2.16	2.76
parliamentary:JJ	2.16	1.66	1.06	2.36	2.02	2.16	2.64
candidates:NNS	2.16	1.80	1.36	1.71	1.52	2.16	2.53
NO_WORD	2.31	0.29	0.40	2.48	0.23	2.31	0.96

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.35 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.34 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.51 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.29 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/7.0 = 0.29
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "law" modifying "amendment" is dropped on aligned hypothesis word "amendment"
-4.00 0.02 NullPunisher.other : was
-4.00 0.44 NullPunisher.other : rejected
1.00 1.00 Quant.equivalent : Replacing the quantifier "an" (negation: false) by an equivalent quantifier "a" (negation: false) preserves truth.
-2.00 1.00 RootEntailment.unalignedRoot : "rejected" not aligned to anything
Hand-tuned score (dot product of above): 0.2442
Threshold: 0.1863

Inference ID: 11

Txt: I recently took a round trip from Abuja to Yola, the capital of Adamawa State and back to Abuja, with a fourteen-seater bus.

Hyp: Abuja is located in Adamawa State. (don't know)

	Abuja NNP	is VBZ	located VBN	Adamawa_State NNP
I:PRP	3.54	1.96	2.24	3.54
recently:RB	3.25	1.66	1.16	3.25
took:VBD	2.76	1.36	1.50	2.76
a:DT	3.45	2.55	2.55	3.45
round_trip:NN	2.65	1.71	1.01	2.63
Abuja:NNP	4.06	2.46	2.46	1.90
Yola:NNP	1.81	2.46	2.38	1.90
the:DT	3.45	2.55	2.55	3.45
capital:NN	2.33	1.71	1.02	2.78
Adamawa_State:NNP	2.39	2.46	2.46	3.57
back:RB	3.15	1.66	1.66	3.25
Abuja:NNP	4.06	2.46	2.46	1.90
a:DT	3.45	2.55	2.55	3.45
fourteen-seater:JJ	2.64	2.36	2.36	2.46
bus:NN	2.44	1.54	0.94	2.63
NO_WORD	0.40	2.48	0.23	0.38

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.29 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.41 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 2.97 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 1.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 1
0.10 0.25 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 1/4.0 = 0.25
-4.00 0.01 NullPunisher.other : is
-4.00 0.44 NullPunisher.other : located
-2.00 1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
Hand-tuned score (dot product of above): -0.2377
Threshold: 0.1863

Inference ID: 12

Txt: Accardo founded the Settimane Musicali Internazionali in Naples and the Cremona String Festival in 1971, and in 1996, he re-founded the Orchestra da Camera Italiana (O.C.I.), whose members are the best pupils of the Walter Stauffer Academy.

Hyp: Accardo was a member of the Walter Stauffer Academy. (don't know)

	Accardo NNP	was VBD	a DT	member NN	the DT	Walter_Stauffer_Academy NNP
Accardo:NNP	4.06	2.46	2.90	2.65	2.90	1.84
founded:VBD	2.76	1.63	2.16	1.08	2.16	2.76
the:DT	3.45	2.55	3.69	2.71	1.26	3.45
Settimane_Musicali_Internazionali:NNP	1.84	2.46	2.90	2.65	2.90	1.84
Naples:NNP	2.39	2.36	2.90	2.46	2.90	2.33
the:DT	3.45	2.55	3.69	2.71	1.26	3.45
Cremona_String_Festival:NNP	1.84	2.46	2.90	2.65	2.90	1.79
1971:CD	2.60	2.90	2.90	2.28	2.90	2.60
1996:CD	2.60	2.90	2.90	2.86	2.90	2.60
he:PRP	4.06	2.46	2.90	2.65	2.90	1.84
re-founded:VBD	2.76	1.63	2.16	1.88	2.16	2.76
the:DT	3.45	2.55	3.69	2.71	1.26	3.45
Orchestra_da_Camera_Italiana:NNP	2.39	2.46	2.90	2.67	2.90	2.24
O.C.I.:NNP	2.58	1.71	2.16	1.88	2.16	2.60
whose:WP$	3.54	2.03	1.63	2.72	1.41	3.54
members:NNS	2.65	1.71	2.16	2.97	2.16	2.53
are:VBP	2.76	1.79	2.16	2.01	1.87	2.76
the:DT	3.45	2.55	3.69	2.71	1.26	3.45
best:JJS	2.64	2.24	2.16	1.79	2.16	2.64
pupils:NNS	2.65	1.71	2.16	1.64	2.16	2.52
the:DT	3.45	2.55	3.69	2.71	1.26	3.45
Walter_Stauffer_Academy:NNP	1.84	2.46	2.90	2.53	2.90	4.06
NO_WORD	0.61	2.58	2.31	0.11	2.31	0.05

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.46 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.25 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 4.00 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.33 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/6.0 = 0.33
-0.10 1.00 NullPunisher.functionWord : a
Hand-tuned score (dot product of above): 0.8857
Threshold: 0.1863

Inference ID: 13

Txt: Airbus could site a design engineering centre in the Midlands region of the UK to take advantage of the availability of skilled engineering staff following the demise of MG Rover, the collapsed UK carmaker.

Hyp: Airbus plans a design engineering centre. (yes)

	Airbus NNP	plans VBZ	a DT	design NN	engineering NN	center NN
Airbus:NNP	4.06	2.38	2.90	2.65	2.66	2.61
could:MD	3.45	2.55	3.69	2.71	2.71	2.63
site:VB	2.76	1.06	2.16	0.77	1.38	0.81
a:DT	3.45	2.55	1.26	2.71	2.71	2.71
design:NN	2.65	0.90	2.16	4.06	0.05	1.00
engineering:NN	2.66	0.83	2.16	0.05	4.06	1.05
center:NN	2.61	1.24	2.16	1.00	1.05	4.06
the:DT	3.45	2.55	3.69	2.71	2.71	2.71
Midlands:NNP	2.21	2.13	2.90	2.66	2.67	2.42
region:NN	2.61	1.54	2.16	1.56	1.95	1.10
the:DT	3.45	2.55	3.69	2.71	2.71	2.71
UK:NNP	2.34	2.46	2.90	2.68	2.70	2.40
to:TO	3.45	2.55	3.69	2.71	2.71	2.71
take:VB	2.76	0.67	2.16	2.01	2.01	1.98
advantage:NN	2.67	1.47	2.16	1.75	1.89	1.85
the:DT	3.45	2.55	3.69	2.71	2.71	2.71
availability:NN	2.65	1.46	2.16	1.68	1.80	1.74
skilled:JJ	2.58	2.30	2.16	1.69	1.19	1.81
engineering:NN	2.66	0.83	2.16	0.05	4.06	1.05
staff:NN	2.66	1.12	2.16	1.51	1.37	1.98
the:DT	3.45	2.55	3.69	2.71	2.71	2.71
demise:NN	2.65	1.17	2.16	1.48	1.92	1.62
MG_Rover:NNP	2.38	2.46	2.90	2.63	2.59	2.55
the:DT	3.45	2.55	3.69	2.71	2.71	2.71
collapsed:JJ	2.64	1.88	2.16	1.89	1.89	1.44
UK:NNP	2.34	2.46	2.90	2.68	2.70	2.40
carmaker:NN	2.64	1.64	2.16	1.61	1.29	1.68
NO_WORD	0.61	0.23	2.31	0.30	0.30	0.04

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.70 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.11 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 5.21 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 6.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 6
0.10 1.00 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 6/6.0 = 1.00
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "region" modifying "center" is dropped on aligned hypothesis word "center"
-2.00 1.00 Modal.dontKnow : possible -> actual
0.50 1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.0978
Threshold: 0.1863

Inference ID: 14

Txt: Alex Dyer, spokesman for the group, stated that Santarchy in Auckland is part of a worldwide phenomenon.

Hyp: Alex Dyer represents Santarchy. (don't know)

	Alex_Dyer NNP	represents VBZ	Santarchy NNP
Alex_Dyer:NNP	4.06	2.41	1.90
spokesman:NN	2.52	0.50	2.56
the:DT	3.45	2.55	3.45
group:NN	2.70	1.06	2.65
stated:VBD	2.76	1.38	2.59
that:IN	3.52	2.44	3.52
Santarchy:NNP	1.90	2.46	4.06
Auckland:NNP	2.34	2.46	2.34
is:VBZ	2.76	0.59	2.76
part:NN	2.69	1.14	2.65
a:DT	3.45	2.55	3.45
worldwide:JJ	2.55	1.90	2.64
phenomenon:NN	2.58	1.09	2.61
NO_WORD	0.61	0.23	0.04

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.35 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.34 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.23 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.67 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/3.0 = 0.67
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Auckland" modifying "Santarchy" is dropped on aligned hypothesis word "Santarchy"
Hand-tuned score (dot product of above): 1.5275
Threshold: 0.1863

Inference ID: 15

Txt: As late as 1799, priests were still being imprisoned or deported to penal colonies and persecution only worsened after the French army led by General Louis Alexandre Berthier captured Rome and imprisoned Pope Pius VI, who would die in captivity in Valence, Drôme, France in August of 1799.

Hyp: Alexandre Berthier died in 1799. (don't know)

	Alexandre_Berthier NNP	died VBD	1799 CD
late:RB	3.25	1.20	2.91
as:RB	3.25	1.66	2.97
1799:CD	2.60	2.28	2.71
priests:NNS	2.65	1.18	2.80
were:VBD	2.76	1.67	3.01
still:RB	3.25	1.57	2.97
being:VBG	2.76	1.58	3.01
imprisoned:VBN	2.76	0.22	2.45
deported:VBN	2.76	0.18	2.89
penal_colonies:NNS	2.49	1.16	2.12
persecution:NN	2.62	1.07	2.69
only:RB	3.25	1.66	2.97
worsened:VBN	2.76	0.08	2.73
the:DT	3.45	2.42	3.11
French:JJ	2.38	3.11	2.77
army:NN	2.65	1.27	2.36
led:VBN	2.76	1.21	2.96
General:NNP	2.65	1.71	2.80
Louis_Alexandre_Berthier:NNP	1.79	2.46	2.54
captured:NNP	2.65	0.80	2.31
Rome:NNP	2.39	2.46	2.54
imprisoned:NNP	2.65	0.43	2.24
Pope_Pius_VI:NNP	1.90	2.46	2.54
who:WP	3.54	2.24	2.90
would:MD	3.45	2.45	3.11
die:VB	2.76	1.62	2.88
captivity:NN	2.65	0.32	2.32
Valence:NNP	2.39	2.46	2.54
Drôme:NNP	1.90	2.36	2.54
France:NNP	2.39	2.46	2.54
in:IN	3.52	2.44	2.90
August_of_1799:CD	2.60	2.28	3.82
NO_WORD	0.61	0.23	1.54

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.25 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.45 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 2.70 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.33 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 1/3.0 = 0.33
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "France" modifying "die" is dropped on aligned hypothesis word "died"
1.00 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 1799
Hand-tuned score (dot product of above): 1.6092
Threshold: 0.1863

Inference ID: 16

Txt: Cauhtemoc Cardenas said during a news conference on 7 June that the visit to Mexico by Salvadoran president Alfredo Cristiani is a visit by "a repressive ruler who oppresses a large sector of his people."

Hyp: Alfredo Cristiani visits Mexico on June 7. (don't know)

	Alfredo_Cristiani NNP	visits VBZ	Mexico NNP	June_7 CD
Cauhtemoc_Cardenas:NNS	1.62	2.46	2.39	2.54
said:VBD	2.76	1.61	2.76	3.01
a:DT	3.45	2.55	3.45	3.11
news_conference:NN	2.54	1.22	2.65	2.80
7_June:CD	2.60	2.90	2.60	1.66
that:IN	3.52	2.44	3.52	2.90
the:DT	3.45	2.55	3.45	3.11
visit:NN	2.65	2.48	2.60	2.69
Mexico:NNP	2.39	2.32	4.06	2.54
by:IN	3.52	2.44	3.52	2.90
Salvadoran:JJ	2.23	3.11	1.89	2.77
president:NN	2.65	0.96	2.57	2.80
Alfredo_Cristiani:NNP	4.06	2.46	2.39	2.54
is:VBZ	2.76	1.64	2.76	3.01
a:DT	3.45	2.55	3.45	3.11
visit:NN	2.65	2.48	2.60	2.69
a:DT	3.45	2.55	3.45	3.11
repressive:JJ	2.55	2.22	2.64	3.03
ruler:NN	2.65	1.00	2.62	2.57
who:WP	3.54	2.24	3.54	2.90
oppresses:VBZ	2.76	1.46	2.76	3.01
a:DT	3.45	2.55	3.45	3.11
large:JJ	2.64	2.21	2.64	3.03
sector:NN	2.65	1.63	2.53	2.80
his:PRP$	3.54	2.15	3.54	2.90
people:NNS	2.65	1.70	2.59	2.80
NO_WORD	0.61	0.23	0.04	0.96

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.41 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.29 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.62 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 4.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 4
0.10 0.50 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/4.0 = 0.50
1.00 1.00 Date.hypFuzzyMatch : fuzzyMatch of hyp date to txt date: June_7 vs. 7_June
Hand-tuned score (dot product of above): 1.4921
Threshold: 0.1863

Inference ID: 17

Txt: Allen was renowned for his skill at scratch-building and creating scenery, and he pioneered the technique of weathering his models to make them look old and more realistic.

Hyp: Allen introduced a new technique of creating realistic scenery. (yes)

	Allen NNP	introduced VBD	a DT	new JJ	technique NN	creating VBG	realistic JJ	scenery NN
Allen:NNP	4.06	2.46	2.90	3.21	2.66	2.46	3.21	2.58
was:VBD	2.76	1.61	2.16	2.50	2.01	1.78	2.50	2.01
renowned:VBN	2.76	0.75	2.16	2.09	1.37	0.79	2.09	1.46
his:PRP$	3.54	2.24	1.63	2.60	2.80	2.24	2.60	2.80
skill:NN	2.67	1.65	2.16	2.28	0.45	0.77	1.44	1.35
scratch-building:VBG	2.76	1.27	2.16	2.13	1.44	0.68	1.98	1.68
creating:VBG	2.76	1.31	2.16	2.27	1.83	1.63	1.79	1.69
scenery:NN	2.58	1.71	2.16	2.46	1.47	1.39	1.98	4.06
he:PRP	4.06	2.46	2.90	3.21	2.66	2.46	3.21	2.58
pioneered:VBD	2.76	0.13	2.16	1.99	1.07	0.38	2.44	1.80
the:DT	3.45	2.55	3.69	2.22	2.71	2.55	2.22	2.71
technique:NN	2.66	1.50	2.16	2.39	4.06	1.43	1.87	1.47
weathering:VBG	2.71	1.47	2.16	2.19	1.69	0.61	2.28	1.52
his:PRP$	3.54	2.24	1.63	2.60	2.80	2.24	2.60	2.80
models:NNS	2.59	0.96	2.16	2.35	1.02	1.39	1.89	1.75
to:TO	3.45	2.55	3.69	2.22	2.71	2.55	2.22	2.71
make:VB	2.67	1.55	2.16	2.50	1.56	0.50	1.57	1.71
them:PRP	3.45	2.24	1.63	2.48	2.80	2.24	2.60	2.80
look:VB	2.67	1.52	2.16	2.44	1.41	1.37	1.85	1.08
old:JJ	2.64	2.12	2.16	3.66	1.89	2.22	1.86	1.31
more:RBR	3.15	1.66	1.81	2.17	2.50	1.66	2.17	2.50
realistic:JJ	2.64	2.33	2.16	1.49	1.30	1.65	3.30	1.41
NO_WORD	0.61	0.23	2.31	0.29	0.04	0.47	0.29	0.04

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.33 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.36 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.44 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 4.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 4
0.10 0.38 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 3/8.0 = 0.38
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "new" modifying "technique"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "more" modifying "realistic" is dropped on aligned hypothesis word "realistic"
1.00 1.00 Hypernym.posWiden : Widening a term (from "pioneered" to "introduced") preserves truth in a positive context
-0.10 1.00 NullPunisher.functionWord : a
-4.00 0.08 NullPunisher.other : new
1.00 1.00 Quant.equivalent : Replacing the quantifier "the" (negation: false) by an equivalent quantifier "a" (negation: false) preserves truth.
Hand-tuned score (dot product of above): 0.7558
Threshold: 0.1863

Inference ID: 18

Txt: Gastrointestinal bleeding can happen as an adverse effect of non-steroidal anti-inflammatory drugs such as aspirin or ibuprofen.

Hyp: Aspirin prevents gastrointestinal bleeding. (don't know)

	Aspirin NNP	prevents VBZ	gastrointestinal JJ	bleeding NN
Gastrointestinal:JJ	2.35	3.11	0.70	2.64
bleeding:NN	2.45	1.02	1.96	4.06
can:MD	3.45	2.67	2.97	2.71
happen:VB	2.57	1.48	3.24	2.01
an:DT	3.45	2.55	2.97	2.71
adverse:JJ	2.40	1.09	1.12	1.03
effect:NN	2.62	0.42	3.20	1.92
non-steroidal:JJ	2.64	2.36	2.41	1.89
anti-inflammatory_drugs:NNS	2.04	1.45	1.47	0.67
aspirin:NN	3.67	2.22	1.35	1.10
ibuprofen:NN	2.54	1.13	1.52	0.73
NO_WORD	0.61	0.23	0.29	0.04

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.28 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.42 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 2.93 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.50 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/4.0 = 0.50
-4.00 0.56 NullPunisher.other : prevents
-2.00 1.00 RootEntailment.unalignedRoot : "prevents" not aligned to anything
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "aspirin"
4.00 1.00 Location.WorldKnowledge.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 1.0869
Threshold: 0.1863

Inference ID: 19

Txt: In 1969, he drew up the report proposing the expulsion from the party of the Manifesto group. In 1984, after Berlinguer's death, Natta was elected as party secretary.

Hyp: Berlinguer succeeded Natta. (don't know)

	Berlinguer NNP	succeeded VBD	Natta NNP
1969:CD	2.60	2.31	2.60
he:PRP	3.54	2.24	3.54
drew:VBD	2.76	1.18	2.76
up:RP	3.45	2.55	3.45
the:DT	3.45	2.55	3.45
report:NN	2.65	1.58	2.65
proposing:VBG	2.72	1.20	2.76
the:DT	3.45	2.55	3.45
expulsion:NN	2.61	1.30	2.65
the:DT	3.45	2.55	3.45
party:NN	2.65	1.22	2.31
the:DT	3.45	2.55	3.45
Manifesto:NNP	1.86	2.36	1.90
group:NN	2.65	1.47	2.65
1984:CD	2.60	2.09	2.60
Berlinguer:NNP	4.06	2.41	1.90
death:NN	2.65	1.11	2.48
Natta:NNP	1.90	2.46	4.06
was:VBD	2.76	1.70	2.76
elected:VBN	2.61	0.03	2.76
party:NN	2.65	1.22	2.31
secretary:NN	2.61	1.12	2.65
NO_WORD	0.61	0.23	0.04

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.33 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.37 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.10 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 1.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 1
0.10 0.33 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 1/3.0 = 0.33
-4.00 0.52 NullPunisher.other : succeeded
-2.00 1.00 RootEntailment.unalignedRoot : "succeeded" not aligned to anything
Hand-tuned score (dot product of above): -0.3280
Threshold: 0.1863

Inference ID: 20

Txt: Blue Mountain Lumber is a subsidiary of Malaysian forestry transnational corporation, Ernslaw One.

Hyp: Blue Mountain Lumber owns Ernslaw One. (don't know)

	Blue_Mountain_Lumber NNP	owns VBZ	Ernslaw_One NNP
Blue_Mountain_Lumber:NNP	4.06	2.46	1.91
is:VBZ	2.76	1.73	2.76
a:DT	3.45	2.55	3.45
subsidiary:NN	2.69	0.62	2.66
Malaysian:JJ	2.38	3.11	2.30
forestry:NN	2.63	1.28	2.63
transnational:JJ	2.64	2.17	2.29
corporation:NN	2.68	1.30	2.46
Ernslaw_One:NNP	1.85	2.46	4.13
NO_WORD	0.61	0.23	0.04

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.36 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.33 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.26 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.33 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 1/3.0 = 0.33
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "corporation" modifying "subsidiary" is dropped on aligned hypothesis word "owns"
-3.00 1.00 Relation.NonMatchPattern : Opposite patterns found between txt and hypothesis in RelationFeaturizer
Hand-tuned score (dot product of above): 0.4150
Threshold: 0.1863

Inference ID: 21

Txt: Blue Mountain Lumber said today it may have to relocate a $30 million project offshore in the wake of an Environment Court decision that blocked it from a planned development site on the Coromandel.

Hyp: Blue Mountain Lumber will locate a development site on the Coromandel. (don't know)

	Blue_Mountain_Lumber NNP	will MD	locate VB	a DT	development NN	site NN	the DT	Coromandel NNP
Blue_Mountain_Lumber:NNP	4.06	2.90	2.46	2.90	2.66	2.68	2.90	2.39
said:VBD	2.76	1.81	1.43	2.16	1.88	1.79	2.16	2.76
today:NN	2.53	2.16	1.48	2.16	1.91	1.84	2.16	2.65
it:PRP	3.54	1.63	2.24	1.63	2.80	2.52	1.63	3.54
may:MD	3.45	3.69	2.55	3.69	2.71	2.71	3.69	3.45
have:VB	2.76	2.25	1.06	2.16	2.01	1.80	2.03	2.76
to:TO	3.45	3.69	2.55	3.69	2.71	2.71	3.52	3.45
relocate:VB	2.76	2.20	3.40	2.16	1.39	1.46	2.16	2.57
a:DT	3.45	3.69	2.55	1.26	2.71	2.71	3.69	3.45
$_30_million:CD	2.60	2.90	2.90	2.90	2.81	2.54	2.90	2.60
project:NN	2.69	2.06	0.95	2.16	0.36	0.67	2.16	2.60
offshore:RB	3.25	1.81	0.98	1.81	1.91	1.89	1.81	3.15
the:DT	3.45	3.69	2.55	3.69	2.71	2.59	1.26	3.45
wake:NN	2.67	1.82	1.29	2.16	1.94	1.53	2.03	2.65
an:DT	3.45	3.69	2.55	4.38	2.71	2.71	3.69	3.45
Environment_Court:NNP	1.72	2.90	2.46	2.90	2.51	2.71	2.90	2.36
decision:NN	2.68	1.97	1.52	2.16	1.29	1.92	2.16	2.65
that:WDT	3.45	3.69	2.38	3.69	2.71	2.71	3.33	3.45
blocked:VBD	2.76	2.30	0.85	2.16	1.92	2.01	2.16	2.71
it:PRP	3.54	1.63	2.24	1.63	2.80	2.52	1.63	3.54
a:DT	3.45	3.69	2.55	1.26	2.71	2.71	3.69	3.45
planned:JJ	2.64	2.16	2.26	2.16	1.36	1.24	2.16	2.49
development:NN	2.66	2.04	1.08	2.16	4.06	1.12	2.16	2.61
site:NN	2.68	1.91	0.04	2.16	1.12	4.06	2.03	2.65
the:DT	3.45	3.69	2.55	3.69	2.71	2.59	1.26	3.45
Coromandel:NNP	2.39	2.90	2.35	2.90	2.61	2.65	2.90	4.06
NO_WORD	0.61	3.59	0.23	2.31	0.30	0.04	2.31	0.20

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.66 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.13 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 5.11 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 6.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 6
0.10 0.62 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 5/8.0 = 0.62
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "planned" modifying "site" is dropped on aligned hypothesis word "site"
1.00 1.00 Factive.factivePassage : Valid pattern: "have" an action X entails X
2.00 1.00 Modal.yes : necessary -> actual
-0.10 0.50 NullPunisher.functionWord : will
0.50 1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 1.5773
Threshold: 0.1863

Inference ID: 22

Txt: Chicago-based Boeing has already scrubbed three delivery slots in 2006 that had been booked by Air Canada.

Hyp: Boeing's headquarters is in Canada. (don't know)

	Boeing NNP	headquarters NN	is VBZ	Canada NNP
Chicago-based:JJ	2.64	1.15	2.36	2.64
Boeing:NNP	4.06	2.65	2.46	1.90
has:VBZ	2.76	2.01	1.58	2.76
already:RB	3.18	2.50	1.66	3.05
scrubbed:VBN	2.76	1.70	1.52	2.76
three:CD	2.60	2.86	2.90	2.60
delivery:NN	2.65	1.86	1.71	2.70
slots:NNS	2.65	1.90	1.71	2.66
2006:CD	2.60	2.86	2.90	2.60
that:WDT	3.45	2.71	2.55	3.45
had:VBD	2.76	2.01	1.50	2.67
been:VBN	2.42	2.01	0.77	2.76
booked:VBN	2.48	1.25	1.54	2.76
Air_Canada:NNP	2.39	2.62	2.46	0.52
NO_WORD	0.01	0.61	0.23	0.38

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.08 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.77 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 1.20 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 1.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 1
0.10 0.25 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 1/4.0 = 0.25
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Chicago-based" modifying "Boeing" is dropped on aligned hypothesis word "Boeing"
-0.00 1.00 NullPunisher.nsubj : headquarters
-4.00 0.36 NullPunisher.other : headquarters
Hand-tuned score (dot product of above): -0.0877
Threshold: 0.1863

Inference ID: 23

Txt: The Kalido Technical Advisory Board members include Boris Evelson, founder and managing partner, Textra Group, Inc., and Bill Inmon, president, Inmon Data Systems.

Hyp: Boris Evelson founded the Kalido Technical Advisory Board. (don't know)

	Boris_Evelson NNP	founded VBD	the DT	Kalido_Technical_Advisory_Board NNP
The:DT	3.45	2.55	2.93	3.45
Kalido_Technical_Advisory_Board:NNP	1.84	2.46	2.90	4.06
members:NNS	2.65	0.94	2.16	2.59
include:VBP	2.76	1.21	2.16	2.76
Boris_Evelson:NNP	4.06	2.46	2.90	1.84
founder:NN	2.65	4.36	2.16	2.65
managing:VBG	2.76	0.91	2.16	2.76
partner:NN	2.65	1.15	2.16	2.58
Textra_Group_,_Inc.:NNP	1.84	2.46	2.90	1.98
Bill_Inmon:NNP	1.72	2.46	2.90	1.86
president:NN	2.57	1.23	2.16	2.59
Inmon_Data_Systems:VBZ	1.95	2.18	2.90	2.01
NO_WORD	0.61	0.23	2.31	0.04

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.55 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.19 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 4.26 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.50 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/4.0 = 0.50
Hand-tuned score (dot product of above): 1.4011
Threshold: 0.1863

Inference ID: 24

Txt: Bountiful arrived after war's end, sailing into San Francisco Bay 21 August 1945. Bountiful was then assigned as hospital ship at Yokosuka, Japan, departing San Francisco 1 November 1945.

Hyp: Bountiful reached San Francisco in August 1945. (yes)

	Bountiful NNP	reached VBD	San_Francisco NNP	August_1945 CD
Bountiful:NNP	4.06	2.46	1.90	2.46
arrived:VBD	2.76	0.47	2.76	2.01
war:NN	2.65	1.39	2.65	1.08
end:NN	2.65	0.96	2.65	2.57
sailing:VBG	2.76	1.32	2.76	2.40
San_Francisco_Bay:NNP	2.65	1.71	1.11	2.80
21_August_1945:CD	2.60	2.70	2.60	5.93
Bountiful:NNP	4.06	2.46	1.90	2.46
was:VBD	2.76	1.69	2.76	3.01
then:RB	3.25	1.58	3.25	2.97
assigned:VBN	2.76	0.92	2.76	2.96
hospital_ship:NN	2.65	1.43	2.58	1.86
Yokosuka:NNP	1.85	2.46	1.90	2.54
Japan:NNP	1.90	2.46	1.90	2.54
departing:NNP	2.56	1.38	2.65	2.80
San_Francisco:NNP	2.39	2.46	3.57	2.54
1_November_1945:CD	2.60	2.70	2.60	1.54
NO_WORD	0.61	0.23	0.04	1.54

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.43 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.27 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.69 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.50 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/4.0 = 0.50
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "1_November_1945" modifying "San_Francisco" is dropped on aligned hypothesis word "San_Francisco"
1.00 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 194508
-4.00 0.36 NullPunisher.other : reached
-2.00 1.00 RootEntailment.unalignedRoot : "reached" not aligned to anything
Hand-tuned score (dot product of above): 0.6148
Threshold: 0.1863

Inference ID: 25

Txt: Bountiful arrived after war's end, sailing into San Francisco Bay 21 August 1945. Bountiful was then assigned as hospital ship at Yokosuka, Japan, departing San Francisco 1 November 1945.

Hyp: Bountiful reached San Francisco on 1 November 1945. (don't know)

	Bountiful NNP	reached VBD	San_Francisco NNP	1_November_1945 CD
Bountiful:NNP	4.06	2.46	1.90	2.54
arrived:VBD	2.76	0.47	2.76	2.01
war:NN	2.65	1.39	2.65	1.08
end:NN	2.65	0.96	2.65	2.57
sailing:VBG	2.76	1.32	2.76	2.40
San_Francisco_Bay:NNP	2.65	1.71	1.11	2.80
21_August_1945:CD	2.60	2.70	2.60	1.67
Bountiful:NNP	4.06	2.46	1.90	2.54
was:VBD	2.76	1.69	2.76	3.01
then:RB	3.25	1.58	3.25	2.97
assigned:VBN	2.76	0.92	2.76	3.01
hospital_ship:NN	2.65	1.43	2.58	1.87
Yokosuka:NNP	1.85	2.46	1.90	2.54
Japan:NNP	1.90	2.46	1.90	2.54
departing:NNP	2.56	1.38	2.65	2.80
San_Francisco:NNP	2.39	2.46	3.57	2.54
1_November_1945:CD	2.60	2.70	2.60	2.71
NO_WORD	0.61	0.23	0.04	0.96

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.30 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.40 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.04 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.50 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/4.0 = 0.50
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Yokosuka" modifying "San_Francisco" is dropped on aligned hypothesis word "San_Francisco"
1.00 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 19451101
-4.00 0.36 NullPunisher.other : reached
-2.00 1.00 RootEntailment.unalignedRoot : "reached" not aligned to anything
Hand-tuned score (dot product of above): 0.3522
Threshold: 0.1863

Inference ID: 26

Txt: The Prime Minister of Spain Zapatero visited Brazil, Argentina, Chile and Uruguay recently, in a effort to build a left axis in South America. The cited countries' South American Presidents agreed to collaborate at international level, particularly in the United Nations , European Union and with Paris, Berlin and Madrid.

Hyp: Brazil is part of the United Nations. (yes)

	Brazil NNP	is VBZ	part NN	the DT	United_Nations NNPS
The:DT	3.45	2.55	2.71	2.93	3.45
Prime_Minister:NNP	2.60	1.71	1.99	2.16	2.47
Spain_Zapatero:NNP	1.57	2.46	2.73	2.90	2.28
visited:VBD	2.76	1.43	1.82	2.16	2.76
Brazil:NNP	4.06	2.46	2.73	2.90	2.39
Argentina:NNP	0.70	2.46	2.71	2.90	2.21
Chile:NNP	0.85	2.46	2.70	2.69	2.39
Uruguay:NNP	0.90	2.46	2.68	2.90	2.38
recently:RB	3.25	1.66	2.31	1.81	3.25
a:DT	3.45	2.55	2.71	3.69	3.45
effort:NN	2.71	1.71	0.51	2.16	2.67
to:TO	3.45	2.55	2.71	3.52	3.45
build:VB	2.53	1.68	1.35	2.16	2.76
a:DT	3.45	2.55	2.71	3.69	3.45
left:JJ	2.64	2.36	1.68	2.16	2.64
axis:NN	2.48	1.43	1.60	2.16	2.65
South_America:NNP	1.90	2.46	2.65	2.90	2.30
The:DT	3.45	2.55	2.71	2.93	3.45
cited:JJ	2.64	2.36	1.76	2.16	2.64
countries:NNS	2.69	1.71	1.97	2.16	2.43
South_American:NNP	2.33	2.46	2.69	2.90	2.26
Presidents:NNP	2.62	1.71	2.05	2.16	2.54
agreed:VBD	2.76	1.40	1.83	2.16	2.76
to:TO	3.45	2.55	2.71	3.52	3.45
collaborate:VB	2.76	1.56	1.84	2.16	2.73
international:JJ	2.60	2.36	1.81	2.16	2.17
level:NN	2.61	1.71	2.01	2.16	2.66
particularly:RB	3.25	1.66	0.30	1.81	3.18
in:IN	3.52	2.01	2.77	2.36	3.52
the:DT	3.45	2.55	2.71	1.26	3.45
United_Nations:NNPS	2.39	2.46	2.69	2.90	4.06
European_Union:NNP	2.41	2.46	2.73	2.90	1.14
Paris:NNP	1.45	2.34	2.27	2.90	2.40
Berlin:NNP	1.40	2.46	2.73	2.90	2.39
Madrid:NNP	1.54	2.46	2.54	2.90	2.39
NO_WORD	0.61	2.58	0.11	2.31	0.05

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.37 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.32 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.49 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.40 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/5.0 = 0.40
-4.00 0.01 NullPunisher.other : is
-4.00 0.14 NullPunisher.other : part
-2.00 1.00 RootEntailment.unalignedRoot : "part" not aligned to anything
Hand-tuned score (dot product of above): 0.2827
Threshold: 0.1863

Inference ID: 27

Txt: Under the headline "Greed instead of quality", Germany's Die Tageszeitung says no good will come of the acquisition of the publisher Berliner Verlag by two British and US-based investment funds.

Hyp: British and US-based investment funds acquire Berliner Verlag. (yes)

	British JJ	US-based JJ	investment_funds NNS	acquire VB	Berliner_Verlag NNP
the:DT	2.97	2.97	2.71	2.55	3.45
headline:NN	3.15	3.21	1.91	1.40	2.61
Greed:NN	2.46	2.40	2.65	2.42	2.38
quality:JJ	2.49	2.61	1.89	2.24	2.64
Germany:NNP	2.95	2.90	2.71	2.46	2.33
Die_Tageszeitung:NN	2.95	2.95	2.52	2.46	1.69
says:VBZ	3.25	3.25	1.92	1.72	2.76
no:DT	2.97	2.97	2.71	2.55	3.45
good:NN	3.21	3.21	1.94	1.35	2.65
will:MD	2.97	2.97	2.71	2.77	3.45
come:VB	3.25	3.25	2.01	1.37	2.76
the:DT	2.97	2.97	2.71	2.55	3.45
acquisition:NN	3.12	2.42	1.94	1.03	2.67
the:DT	2.97	2.97	2.71	2.55	3.45
publisher:NN	3.00	2.53	1.96	1.20	2.59
Berliner_Verlag:NN	2.95	2.95	2.64	2.46	3.68
two:CD	2.64	2.64	2.86	2.90	2.60
British:NNS	0.28	2.41	2.68	2.34	2.39
US-based:JJ	1.81	3.30	2.41	2.40	2.38
investment_funds:NNS	3.21	2.98	4.06	1.58	2.64
NO_WORD	0.29	0.50	0.61	0.23	0.04

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.37 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.32 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.51 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 4.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 4
0.10 0.40 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/5.0 = 0.40
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "British" modifying "investment_funds"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "publisher" modifying "Berliner_Verlag" is dropped on aligned hypothesis word "Berliner_Verlag"
1.00 1.00 Factive.positiveStatement : Valid pattern: "come" X entails X
Hand-tuned score (dot product of above): 1.0573
Threshold: 0.1863

Inference ID: 28

Txt: As much as 200 mm of rain have been recorded in portions of British Columbia , on the west coast of Canada since Monday.

Hyp: British Columbia is located in Canada. (yes)

	British_Columbia NNP	is VBZ	located VBN	Canada NNP
As:RB	3.25	1.24	1.66	3.25
much:JJ	2.64	2.36	2.36	2.64
as:IN	3.52	2.01	2.44	3.52
200:CD	2.60	2.90	2.58	2.60
mm:NN	2.65	1.71	1.71	2.65
rain:NN	2.65	1.71	0.94	2.61
have:VBP	2.76	1.46	1.45	2.76
been:VBN	2.76	0.77	1.57	2.76
recorded:VBN	2.76	1.70	1.14	2.76
portions:NNS	2.65	1.71	0.71	2.76
British_Columbia:NNP	4.06	2.46	2.46	1.90
the:DT	3.45	2.55	2.55	3.45
west_coast:NN	2.58	1.71	0.17	2.65
Canada:NNP	1.90	2.46	2.26	4.06
Monday:NNP	2.39	2.46	2.39	2.15
NO_WORD	0.40	2.48	0.23	0.38

Inference ID: 29

Txt: Dr Wood led a courageous and committed team in the fight to save 28 patients suffering from between two and 92 per cent body burns, deadly infections and delayed shock. As well as receiving much praise from both her own patients and the media, she also attracted controversy among other burns surgeons due to the fact that spray-on skin had not yet been subjected to clinical trials.

Hyp: Burns surgeons approve Dr Wood's spray-on skin. (don't know)

	Burns NNP	surgeons NNS	approve VBP	Dr NNP	Wood NNP	spray-on JJ	skin NN
Dr:NNP	2.65	1.90	1.71	4.06	2.65	2.46	1.90
Wood:NNP	2.35	2.62	2.46	2.65	4.06	3.21	2.65
led:VBD	2.76	2.01	1.57	2.01	2.64	2.50	2.01
a:DT	3.45	2.71	2.55	2.71	3.45	2.22	2.71
courageous:JJ	2.58	1.43	2.31	1.89	2.64	1.68	1.69
committed:JJ	2.64	1.89	2.08	1.89	2.64	1.86	1.89
team:NN	2.67	1.47	1.58	1.90	2.68	2.46	1.87
the:DT	3.45	2.71	2.55	2.71	3.45	2.22	2.71
fight:NN	2.66	1.92	1.07	1.90	2.67	2.46	1.67
to:TO	3.45	2.71	2.55	2.71	3.45	2.22	2.71
save:VB	2.76	1.93	1.26	2.01	2.76	2.50	1.80
28:CD	2.60	2.86	2.52	2.86	2.60	2.90	2.86
patients:NNS	2.45	0.26	1.41	1.90	2.62	2.36	0.59
suffering:VBG	2.64	1.24	1.52	2.01	2.76	2.36	1.52
between:QUANT_MOD	3.45	2.54	2.55	2.71	3.45	2.16	2.71
two_and_92:CD	2.60	2.80	2.90	2.86	2.60	2.90	2.86
cent:NN	2.58	1.93	1.61	1.90	2.69	2.46	1.94
body:NN	2.50	0.80	1.33	1.90	2.44	2.46	0.02
burns:NNS	3.34	2.10	2.14	2.65	2.35	3.21	2.52
deadly:JJ	2.64	1.37	2.29	1.89	2.64	1.86	0.98
infections:NNS	2.65	0.19	1.65	1.90	2.65	2.37	0.47
delayed:JJ	2.64	1.89	1.19	1.89	2.64	1.70	1.89
shock:NN	2.65	1.84	1.58	1.90	2.56	2.46	1.39
well:RB	3.25	2.50	1.66	2.50	3.04	2.17	2.50
as:RB	3.25	2.50	1.66	2.50	3.25	2.17	2.50
receiving:VBG	2.76	1.61	1.59	2.01	2.76	2.45	2.01
much:JJ	2.55	1.89	2.36	1.89	2.64	1.86	1.89
praise:NN	2.58	1.91	1.16	1.90	2.66	2.22	1.95
both:CC	3.36	2.71	2.55	2.71	3.24	2.22	2.71
her:PRP$	3.54	2.80	2.24	2.63	3.54	2.60	2.80
own:JJ	2.64	1.89	2.36	1.89	2.52	1.86	1.77
patients:NNS	2.45	0.26	1.41	1.90	2.62	2.36	0.59
the:DT	3.45	2.71	2.55	2.71	3.45	2.22	2.71
media:NNS	2.59	1.67	1.71	1.90	2.66	2.46	1.73
she:PRP	2.35	2.62	2.46	2.65	4.06	3.21	2.65
also:RB	3.25	2.50	1.58	2.50	3.25	2.17	2.50
attracted:VBD	2.76	1.97	1.35	2.01	2.76	2.36	2.01
controversy:NN	2.66	1.68	1.03	1.90	2.67	2.46	1.87
other:JJ	2.64	1.89	2.36	1.89	2.64	1.86	1.89
burns:NNS	3.34	2.10	2.14	2.65	2.35	3.21	2.52
surgeons:VBZ	2.44	2.22	1.38	2.01	2.76	2.19	0.87
due:JJ	2.64	1.89	2.27	1.73	2.64	1.86	1.88
the:DT	3.45	2.71	2.55	2.71	3.45	2.22	2.71
fact:NN	2.68	1.94	1.56	1.90	2.70	2.46	1.84
that:IN	3.52	2.77	2.44	2.77	3.52	2.16	2.77
spray-on:JJ	2.64	1.58	2.30	1.89	2.64	3.30	1.89
skin:NN	2.52	0.73	1.64	1.90	2.65	2.46	4.06
had:VBD	2.76	2.01	1.62	2.01	2.64	2.50	2.01
not:RB	3.25	2.50	1.66	2.50	3.13	2.17	2.50
yet:RB	3.25	2.50	1.66	2.50	3.25	2.17	2.50
been:VBN	2.48	2.01	1.65	2.01	2.76	2.50	1.80
subjected:VBN	2.76	1.67	1.55	2.01	2.76	2.45	1.36
clinical_trials:NNS	2.65	0.94	1.48	1.90	2.65	2.46	0.97
NO_WORD	0.30	0.61	0.23	0.30	0.01	0.29	0.04

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.49 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.22 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 4.22 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 4.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 4
0.10 0.29 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/7.0 = 0.29
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "other" modifying "burns" is dropped on aligned hypothesis word "Burns"
-4.00 0.67 NullPunisher.other : approve
3.00 1.00 Relation.PatternMatched : Pattern found between txt and hypothesis in RelationFeaturizer: 1: (Relation Name = is, Arg 0 = Wood, Arg 1 = Dr,Relation Name = is, Arg 0 = Wood, Arg 1 = Dr)
-2.00 1.00 RootEntailment.unalignedRoot : "approve" not aligned to anything
Hand-tuned score (dot product of above): 0.6508
Threshold: 0.1863

Inference ID: 30

Txt: In announcing plans today to prepare the nation for combating a future worldwide wave of bird flu, President Bush used vocabulary and tactics that are familiar from his confrontation with global terrorism.

Hyp: Bush supports global terrorism. (don't know)

	Bush NNP	supports VBZ	global JJ	terrorism NN
announcing:VBG	2.76	1.58	2.50	1.97
plans:NNS	2.76	1.30	2.39	1.85
today:NN	2.71	1.65	2.04	1.90
to:TO	3.45	2.55	2.22	2.71
prepare:VB	2.76	1.42	2.19	1.80
the:DT	3.45	2.55	2.22	2.71
nation:NN	2.74	1.13	2.21	1.70
combating:VBG	2.76	0.99	2.43	0.89
a:DT	3.45	2.55	2.22	2.71
future:JJ	2.64	1.84	1.15	1.70
worldwide:JJ	2.64	2.20	0.63	1.50
wave:NN	2.71	1.61	1.77	1.76
bird:NN	2.33	1.71	2.46	1.93
flu:NN	2.66	1.71	2.46	1.80
President_Bush:NNP	1.40	2.46	3.21	2.67
used:VBD	2.55	1.50	2.50	1.84
vocabulary:NN	2.65	1.71	2.46	1.70
tactics:NNS	2.67	1.16	2.46	1.30
that:WDT	3.45	2.55	2.22	2.71
are:VBP	2.76	1.69	2.50	2.01
familiar:JJ	2.64	2.36	1.71	1.70
his:PRP$	3.42	2.24	2.60	2.80
confrontation:NN	2.67	1.14	2.46	0.38
global:JJ	2.64	2.23	3.30	1.73
terrorism:NN	2.68	1.20	2.30	4.06
NO_WORD	0.61	0.23	0.29	0.04

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.30 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.40 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.03 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.50 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/4.0 = 0.50
-4.00 0.45 NullPunisher.other : supports
-2.00 1.00 RootEntailment.unalignedRoot : "supports" not aligned to anything
Hand-tuned score (dot product of above): -0.1776
Threshold: 0.1863

Inference ID: 31

Txt: Scott Island was discovered and landed upon in December 1902 by Captain William Colbeck commander of the Morning, relief ship for Capt. Robert F. Scott's expedition.

Hyp: Capt. Scott reached Scott Island in December 1902. (don't know)

	Capt. NNP	Scott NNP	reached VBD	Scott_Island NNP	December_1902 CD
Scott_Island:NNP	2.65	1.57	2.46	4.06	2.51
was:VBD	2.01	2.76	1.69	2.76	3.01
discovered:VBN	2.01	2.71	1.02	2.68	2.11
landed:VBD	1.94	2.76	0.83	2.76	2.44
upon:RP	2.61	3.36	2.55	3.45	3.11
December_1902:CD	2.86	2.60	2.54	2.57	2.71
Captain:NNP	2.19	2.51	1.71	2.38	2.80
William_Colbeck:NNP	2.65	1.90	2.46	2.36	2.54
commander:NNP	1.90	2.52	1.56	2.39	2.80
the:DT	2.71	3.45	2.55	3.45	3.11
Morning:NN	1.90	2.66	1.66	2.62	2.80
relief:NN	1.90	2.66	1.30	2.66	2.62
ship:NN	1.90	2.50	1.43	2.59	2.41
Capt.:NNP	4.06	2.48	1.71	2.65	2.80
Robert_F._Scott:NNP	2.65	0.70	2.46	2.17	2.48
expedition:NN	1.90	2.65	1.51	2.57	1.99
NO_WORD	0.30	0.61	0.23	0.04	1.54

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.32 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.38 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.21 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.40 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/5.0 = 0.40
1.00 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 190212
-4.00 0.36 NullPunisher.other : reached
3.00 1.00 Relation.PatternMatched : Pattern found between txt and hypothesis in RelationFeaturizer: 1: (Relation Name = is, Arg 0 = Robert_F._Scott, Arg 1 = Capt.,Relation Name = is, Arg 0 = Scott, Arg 1 = Capt.)
-2.00 1.00 RootEntailment.unalignedRoot : "reached" not aligned to anything
Hand-tuned score (dot product of above): 0.9245
Threshold: 0.1863

Inference ID: 32

Txt: Carl Smith collided with a concrete lamp-post while skating and suffered a skull fracture that caused a coma. When he failed to regain consciousness, his parents on August 8 consented to his life support machine being turned off.

Hyp: Carl Smith died on August 8. (yes)

	Carl_Smith NNP	died VBD	August_8 CD
Carl_Smith:NNP	4.06	2.46	2.45
collided:VBD	2.67	0.87	2.74
a:DT	3.45	2.55	3.11
concrete:JJ	2.55	2.36	2.95
lamp-post:JJ	2.51	2.05	2.79
while:NN	2.68	1.62	2.80
skating:NN	2.65	1.49	2.60
suffered:VBD	2.76	0.08	2.86
a:DT	3.45	2.55	3.11
skull:NN	2.60	1.20	2.72
fracture:NN	2.66	1.06	2.65
that:WDT	3.45	2.55	3.11
caused:VBD	2.66	0.59	2.89
a:DT	3.45	2.55	3.11
coma:NN	2.65	0.10	2.69
When:WRB	3.45	2.33	3.11
he:PRP	4.06	2.46	2.45
failed:VBD	2.76	0.18	3.01
to:TO	3.45	2.55	3.11
regain:VB	2.76	0.95	3.01
consciousness:NN	2.63	0.88	2.80
his:PRP$	3.54	2.12	2.90
parents:NNS	2.38	1.14	2.59
August_8:CD	2.51	2.90	2.71
consented:VBD	2.63	1.17	2.92
his:PRP$	3.54	2.12	2.90
life_support:NN	2.57	1.71	2.80
machine:NN	2.56	1.61	2.57
being:VBG	2.76	1.58	3.01
turned:VBN	2.76	0.50	3.01
off:RP	3.45	2.55	3.11
NO_WORD	0.61	0.23	0.96

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.30 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.40 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 2.96 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.67 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/3.0 = 0.67
1.00 1.00 Date.matchDatesByGraph : hyp/txt matching, by graph: August_8 and children
-1.00 1.00 Hypernym.posNarrow : Narrowing a term (from "suffered" to "died") does NOT preserve truth in a positive context
Hand-tuned score (dot product of above): 1.2026
Threshold: 0.1863

Inference ID: 33

Txt: As leaders gather in Argentina ahead of this weekends regional talks, Hugo Chávez, Venezuela's populist president, is using an energy windfall to win friends and promote his vision of 21st-century socialism.

Hyp: Chávez is a follower of socialism. (yes)

	Chávez NNP	is VBZ	a DT	follower NN	socialism NN
As:IN	3.52	2.01	2.08	2.77	2.77
leaders:NNS	2.58	1.71	2.16	3.79	1.16
gather:VBP	2.62	1.64	2.16	1.77	2.01
Argentina:NNP	2.39	2.46	2.90	2.60	2.55
this:DT	3.45	2.26	3.69	2.71	2.71
weekends:JJ	2.64	2.36	2.16	1.89	1.84
regional:JJ	2.64	2.36	2.16	1.79	1.64
talks:NNS	2.65	1.71	2.16	1.97	1.78
Hugo_Chávez:NNP	0.92	2.46	2.90	2.48	2.66
Venezuela:NNP	2.39	2.46	2.90	2.55	2.65
populist:JJ	2.64	2.36	2.16	1.49	0.15
president:NN	2.65	1.71	2.16	1.73	1.93
is:VBZ	2.76	1.63	2.16	2.01	2.01
using:VBG	2.76	1.71	2.16	1.96	2.01
an:DT	3.45	2.55	4.38	2.71	2.71
energy:NN	2.65	1.71	2.16	1.91	1.93
windfall:NN	2.65	1.71	2.16	1.50	1.47
to:TO	3.45	2.55	3.69	2.71	2.71
win:VB	2.76	1.50	2.16	1.63	1.64
friends:NNS	2.65	1.71	2.16	1.11	1.82
promote:VB	2.76	1.67	2.16	1.85	1.38
his:PRP$	3.54	1.45	1.63	2.80	2.80
vision:NN	2.65	1.71	2.16	1.93	0.86
21st-century:JJ	2.64	2.36	2.16	1.89	1.23
socialism:NN	2.65	1.71	2.16	1.42	4.06
NO_WORD	0.61	2.58	2.31	0.11	0.05

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.19 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.55 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 2.40 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 2.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 2
0.10 0.20 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 1/5.0 = 0.20
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "21st-century" modifying "socialism" is dropped on aligned hypothesis word "socialism"
1.00 1.00 Factive.inPositiveEmbedding : embedded positive text
-0.10 1.00 NullPunisher.functionWord : a
-2.00 1.00 RootEntailment.poorlyAlignedRoot : "follower" aligned badly to "promote"
Hand-tuned score (dot product of above): 0.3909
Threshold: 0.1863

Inference ID: 34

Txt: Parviz Davudi was representing Iran at a meeting of the Shanghai Co-operation Organisation (SCO), the fledgling association that binds Russia, China and four former Soviet republics of central Asia together to fight terrorism.

Hyp: China is a member of SCO. (yes)

	China NNP	is VBZ	a DT	member NN	SCO NNP
Parviz_Davudi:NNP	2.39	2.46	2.90	2.65	1.84
was:VBD	2.76	1.62	2.16	2.01	2.76
representing:VBG	2.76	0.59	2.16	1.34	2.76
Iran:NNP	0.90	2.46	2.90	2.61	2.39
a:DT	3.45	2.55	1.26	2.71	3.45
meeting:NN	2.57	1.71	2.16	0.70	2.65
the:DT	3.45	2.55	3.69	2.71	3.45
Shanghai:NNP	1.13	2.46	2.90	2.60	2.39
Co-operation:NNP	2.70	1.71	2.16	1.98	2.65
Organisation:NNP	2.72	1.71	2.16	2.00	2.65
SCO:NNP	2.39	2.46	2.90	2.65	4.06
the:DT	3.45	2.55	3.69	2.71	3.45
fledgling:JJ	2.64	2.36	2.16	1.87	2.64
association:NN	2.72	1.71	2.16	1.25	2.65
that:WDT	3.36	2.55	3.69	2.71	3.45
binds:VBZ	2.59	1.45	2.16	1.95	2.76
Russia:NNP	1.00	2.46	2.90	2.61	2.39
China:NNP	4.06	2.46	2.90	2.63	2.39
four:CD	2.60	2.90	2.90	2.78	2.60
former:JJ	2.64	2.36	2.16	1.61	2.64
Soviet:JJ	2.31	3.11	2.90	2.50	2.38
republics:NNS	2.68	1.71	2.16	1.90	2.65
central:JJ	2.50	2.36	2.16	1.34	2.64
Asia:NNP	1.23	2.46	2.90	2.62	2.27
together:RB	3.25	1.66	1.81	2.26	3.25
to:TO	3.45	2.55	3.69	2.71	3.29
fight:VB	2.76	1.69	2.16	1.76	2.76
terrorism:NN	2.67	1.71	2.16	1.88	2.65
NO_WORD	0.61	2.58	2.31	0.11	0.05

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.34 Alignment.isGood : Weight for score based on closeness to 'good' threshold
-1.00 0.35 Alignment.isBad : Weight for score based on closeness to 'bad' threshold
1.00 3.35 Alignment.score.scaled : Alignment score scaled by exponentiated hypothesis size.
0.10 3.00 Alignment.hypSpan : The largest number of contiguously aligned words in the hypothesis is 3
0.10 0.40 Alignment.txtSpan : Maximum contiguously aligned span in the text scaled by the hypothesis length is 2/5.0 = 0.40
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Organisation" modifying "meeting" is dropped on aligned hypothesis word "member"
Hand-tuned score (dot product of above): 0.9824
Threshold: 0.1863

Inference ID: 35

Txt: A leading human rights group on Wednesday identified Poland and Romania as the likely locations in eastern Europe of secret prisons where al-Qaeda suspects are interrogated by the Central Intelligence Agency.

Hyp: CIA secret prisons were located in Eastern Europe. (yes)

	CIA NNP	secret NN	prisons NNS	were VBD	located VBN	Eastern_Europe NNP
A:DT	3.45	2.71	2.71	2.55	2.55	3.45
leading:JJ	2.64	1.83	1.77	2.36	1.98	2.64
human:JJ	2.64	1.39	1.36	2.36	2.36	2.64
rights:NNS	2.66	1.93	1.62	1.71	1.65	2.71
group:NN	2.54	1.98	1.87	1.71	1.42	2.78
Wednesday:NNP	2.38	2.59	2.67	2.39	2.46	2.39
identified:VBD	2.76	1.13	1.93	1.66	0.90	2.76
Poland:NNP	2.40	2.67	2.41	2.46	2.13	1.86
Romania:NNP	2.38	2.64	2.47	2.46	2.22	1.84
the:DT	3.45	2.71	2.71	2.42	2.55	3.45
likely:JJ	2.64	1.89	1.83	2.36	2.30	2.64
locations:NNS	2.73	2.01	1.44	1.71	1.83	2.66
eastern_Europe:NN	2.41	2.68	2.61	2.46	2.46	4.08
secret:JJ	2.64	2.49	1.78	2.02	2.17	2.64
prisons:NNS	2.66	1.81	4.06	1.71	1.57	2.61
where:WRB	3.45	2.48	2.71	1.38	2.55	3.45
al-Qaeda:NNP	1.76	2.65	2.67	2.46	2.18	2.43
suspects:NNS	2.65	0.86	1.21	1.71	1.43	2.59
are:VBP	2.76	1.92	2.01	0.41	1.57	2.76
interrogated:VBN	2.76	0.99	1.77	1.51	1.22	2.63
the:DT	3.45	2.71	2.71	2.42	2.55	3.45
Central_Intelligence_Agency:NNP	0.09	2.64	2.66	2.46