Txt/Hyp term similarities

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

Lexical resource summary:
Acronym: AcronymLexicalResource
BasicWN: null
Country: CountryLexicalResource
Coref: CorefLexicalResource
Cyc: null
DekangLin: DekangLinLexicalResource
EditDistance: EditDistanceLexicalResource
Google: null
InfoMap: InfoMapLexicalResource
Morpho: MorphoLexicalResource
NomBank: NomBankLexicalResource
Number: NumberLexicalResource
Ordinal: OrdinalLexicalResource
Preposition: PrepositionLexicalResource
Ravichandran: RavichandranLexicalResource
LeacockChodorowWN: null
JiangConrathWN: JiangConrathWNLexicalResource
ResnikWN: null
LinWN: LinWNLexicalResource
HirstWN: null
AdaptedLeskWN: null
EdgeWN: null
StringSim: StringSimLexicalResource
WebLexicalEntailment: null
CorpusLexicalEntailment: null
WordNet: null
WNAntonymy: WNAntonymyLexicalResource
WNHypernymy: WNHypernymyLexicalResource
WNSynonymy: WNSynonymyLexicalResource

Inference ID: 3

Txt: ECB spokeswoman, Regina Schueller, declined to comment on a report in Italy's La Repubblica newspaper that the ECB council will discuss Mr. Fazio's role in the takeover fight at its Sept. 15 meeting.

Hyp: Regina Shueller works for Italy's La Repubblica newspaper. (don't know)

	Regina_Shueller NNP	works VBZ	Italy NNP	La_Repubblica NNP	newspaper NN
NO_WORD	0.95	0.17	0.12	0.05	0.05
ECB:NN	1.07	1.07	0.99	0.82	1.07
spokeswoman:NN	0.95	0.69	1.07	1.08	0.70
Regina_Schueller:NNP	1.16	1.07	0.99	0.93	1.07
declined:VBD	1.25	0.76	1.16	1.16	0.76
to:TO	1.16	1.25	1.25	1.25	1.00
comment:VB	1.25	0.70	1.16	1.16	0.65
a:DT	1.07	1.25	1.25	1.25	1.00
report:NN	0.99	0.70	1.13	1.13	0.43
Italy:NNP	0.94	1.07	1.82	0.99	1.09
La_Repubblica:NNP	1.06	1.07	0.99	1.82	1.10
newspaper:NN	1.57	0.78	1.09	1.10	1.82
that:IN	1.25	1.24	1.54	1.57	1.31
the:DT	0.95	1.25	1.25	1.25	1.00
ECB:NNP	1.07	1.07	0.99	0.82	1.07
council:NN	1.25	0.68	1.12	1.11	0.85
will:MD	1.16	1.42	1.23	1.25	1.00
discuss:VB	0.82	0.58	1.16	1.16	0.80
Mr._Fazio:NNP	1.07	1.07	1.08	0.90	1.19
role:NN	1.25	0.58	1.13	1.15	0.94
the:DT	1.06	1.25	1.25	1.25	1.00
takeover:NN	1.07	0.82	1.13	1.12	0.80
fight:NN	1.07	0.82	1.12	1.11	0.90
its:PRP$	1.07	0.69	1.07	1.08	0.70
Sept.:NNP	0.99	1.07	1.04	1.04	1.17
15:CD	1.24	1.26	1.24	1.24	1.29
meeting:NN	1.07	0.82	1.14	1.13	0.91

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.77 Alignment.score
1.00 0.55 Alignment.isGood
-1.00 0.42 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.60 Alignment.txtSpan
-1.00 1.00 Apposition.mismatch : Regina_Shueller doesn't seem in relation with "newspaper"
-4.00 0.30 NullPunisher.other : works
-2.00 1.00 RootEntailment.unalignedRoot : "works" not aligned to anything
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Italy"
4.00 1.00 Location.WorldKnowledge.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 2.9410
Threshold: -0.1576

Inference ID: 4

Txt: Meanwhile, in an exclusive interview with a TIME journalist, the first one-on-one session given to a Western print publication since his election as president of Iran earlier this year, Ahmadinejad attacked the "threat" to bring the issue of Iran's nuclear activity to the UN Security Council by the US, France, Britain and Germany.

Hyp: Ahmedinejad was attacked by the US, France, Britain and Germany. (don't know)

	Ahmedinejad NNP	was VBD	attacked VBN	the DT	US NNP	France NNP	Britain NNP	Germany NNP
Meanwhile:RB	1.19	0.91	0.90	1.09	1.47	1.43	1.47	1.47
an:DT	1.00	1.21	1.25	1.31	1.25	1.25	1.25	1.25
exclusive:JJ	0.88	0.96	0.91	0.76	1.13	1.13	1.13	1.13
interview:NN	0.80	0.82	0.67	0.71	1.17	1.12	1.13	1.10
a:DT	1.00	1.25	1.25	1.31	1.25	1.25	1.25	1.25
TIME:JJ	0.88	0.96	0.96	0.67	1.13	1.13	1.13	1.13
journalist:NN	0.82	0.82	0.65	0.71	1.16	1.10	1.10	1.09
the:DT	1.00	1.25	1.25	0.32	1.25	1.25	1.25	1.25
first:JJ	1.13	1.22	1.22	1.02	1.05	1.03	1.05	1.05
one-on-one:JJ	0.84	0.96	0.96	0.76	1.13	1.13	1.13	1.12
session:NN	0.82	0.82	0.80	0.71	1.19	1.13	1.11	1.10
given:VBN	0.91	0.85	0.55	0.76	1.16	1.16	1.13	1.13
to:TO	1.00	1.25	1.25	1.26	1.25	1.25	1.25	1.25
a:DT	1.00	1.25	1.25	1.31	1.25	1.25	1.25	1.25
Western:JJ	1.13	1.22	1.22	1.02	1.05	1.05	0.98	1.01
print:NN	0.82	0.82	0.82	0.71	1.14	1.04	1.07	1.07
publication:NN	0.82	0.82	0.82	0.71	1.20	1.14	1.08	1.14
his:PRP$	1.30	0.90	0.97	1.11	1.51	1.55	1.55	1.55
election:NN	0.82	0.82	0.55	0.71	1.18	1.12	1.09	1.11
president:NN	0.77	0.82	0.55	0.71	1.18	1.10	1.10	1.10
Iran:NNP	1.07	1.04	1.07	0.96	0.70	0.97	0.19	0.21
earlier:RBR	1.21	0.91	0.87	1.09	1.47	1.45	1.47	1.44
this:DT	1.00	1.22	1.25	1.21	1.25	1.25	1.23	1.25
year:NN	0.82	0.78	0.82	0.71	1.25	1.17	1.19	1.16
Ahmadinejad:NNP	1.24	0.82	0.76	0.71	1.07	1.07	1.07	1.07
attacked:VBD	0.87	0.82	1.50	0.76	1.16	1.13	1.15	1.16
the:DT	1.00	1.25	1.25	0.32	1.25	1.25	1.25	1.25
threat:NN	0.80	0.82	0.64	0.63	1.13	1.08	1.08	1.04
to:TO	1.00	1.25	1.25	1.26	1.25	1.25	1.25	1.25
bring:VB	0.91	0.88	0.58	0.76	1.16	1.10	1.09	1.13
the:DT	1.00	1.25	1.25	0.32	1.25	1.25	1.25	1.25
issue:NN	0.82	0.82	0.72	0.71	1.22	1.13	1.17	1.16
Iran:NNP	1.07	1.04	1.07	0.96	0.70	0.97	0.19	0.21
nuclear:JJ	0.88	0.96	0.94	0.76	1.13	1.13	1.13	1.13
activity:NN	0.81	0.82	0.76	0.71	1.34	1.24	1.25	1.24
the:DT	1.00	1.25	1.25	0.32	1.25	1.25	1.25	1.25
UN_Security_Council:NNP	1.07	1.07	1.07	0.96	1.05	1.01	1.02	1.01
the:DT	1.00	1.25	1.25	0.32	1.25	1.25	1.25	1.25
US:NNP	1.07	1.03	1.07	0.96	1.82	0.71	0.72	0.72
France:NNP	1.07	1.07	1.04	0.96	0.71	1.82	0.12	0.04
Britain:NNP	1.07	1.07	1.06	0.96	0.72	0.12	1.82	0.10
Germany:NNP	1.07	1.07	1.07	0.96	0.72	0.04	0.10	1.82
NO_WORD	0.15	1.57	0.17	0.82	0.21	0.55	0.55	0.55

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 2.08 Alignment.score
1.00 0.62 Alignment.isGood
-1.00 0.35 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 6.00 Alignment.hypSpan
0.10 0.62 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "interview" modifying "attacked" is dropped on aligned hypothesis word "attacked"
-0.10 0.50 NullPunisher.functionWord : was
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "US"
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "France"
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Britain"
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Germany"
0.50 1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 7.8586
Threshold: -0.1576

Inference ID: 11

Txt: The chaotic situation unleashed in Bogota last night, with the assasination of Justice Carlos Valencia, began on 28 July in Medellin, when motorized paid assasins murdered third public order Judge Maria Elena Diaz.

Hyp: Justice Carlos Valencia was killed on 28 July. (don't know)

	Justice_Carlos_Valencia NNP	was VBD	killed VBN	28 CD	July NNP
The:DT	1.25	1.25	1.25	1.18	1.25
chaotic:JJ	1.13	0.96	0.90	1.13	1.13
situation:NN	1.11	0.82	0.66	1.29	1.17
unleashed:VBN	1.16	0.70	0.25	1.39	1.16
Bogota:NNP	0.83	1.07	1.07	1.20	0.99
last:JJ	1.05	1.12	1.22	1.04	1.05
night:NN	1.05	1.07	0.97	1.17	0.74
the:DT	1.25	1.25	1.25	1.18	1.25
assasination:NN	1.07	0.82	0.82	1.29	1.07
Justice_Carlos_Valencia:NNP	1.82	1.07	1.07	1.20	1.06
began:VBD	1.16	0.90	0.50	1.39	1.16
28:CD	1.24	1.30	1.30	1.25	1.07
July:NNP	1.06	1.07	1.07	1.03	1.82
Medellin:NNP	0.84	1.07	1.04	1.20	1.00
when:WRB	1.25	1.22	1.25	1.18	1.25
motorized:VBN	1.16	0.66	0.42	1.39	1.16
paid:VBD	1.16	0.90	0.64	1.37	1.16
assasins:NNS	1.07	0.82	0.82	1.29	1.07
murdered:VBN	1.16	0.81	0.40	1.39	1.16
third:JJ	1.05	1.22	1.18	0.84	1.05
public:JJ	1.13	0.96	0.92	1.12	1.09
order:NN	1.16	0.82	0.80	1.29	1.14
Judge_Maria_Elena_Diaz:NNP	0.62	1.07	1.07	1.20	1.07
NO_WORD	0.15	1.57	0.17	0.52	0.09

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.51 Alignment.score
1.00 0.48 Alignment.isGood
-1.00 0.49 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.40 Alignment.txtSpan
0.50 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 07/28/1000
0.50 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 07/28/1000
-0.10 0.50 NullPunisher.functionWord : was
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Justice_Carlos_Valencia"
Hand-tuned score (dot product of above): 3.6938
Threshold: -0.1576

Inference ID: 12

Txt: He met U.S. President, George W. Bush, in Washington and British Prime Minister, Tony Blair, in London.

Hyp: Washington is part of London. (don't know)

	Washington NNP	is VBZ	part NN	London NNP
He:PRP	1.55	0.97	1.30	1.55
met:VBD	1.16	0.78	0.80	1.16
U.S.:NNP	0.65	1.07	1.28	0.93
President:NNP	1.12	0.82	1.04	1.12
George_W._Bush:NNP	1.00	1.07	1.21	1.00
Washington:NNP	1.82	1.07	1.25	0.64
British:NNP	1.05	1.07	1.21	1.05
Prime_Minister:NNP	1.06	0.82	0.95	1.08
Tony_Blair:NNP	0.97	1.07	1.17	0.97
London:NNP	0.64	1.07	1.25	1.82
NO_WORD	0.28	1.43	0.12	0.04

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.30 Alignment.score
1.00 0.43 Alignment.isGood
-1.00 0.54 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.25 Alignment.txtSpan
-4.00 0.14 NullPunisher.other : part
-0.10 0.50 NullPunisher.functionWord : is
-2.00 1.00 RootEntailment.unalignedRoot : "part" not aligned to anything
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Washington"
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "London"
4.00 1.00 Location.WorldKnowledge.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 4.5180
Threshold: -0.1576

Inference ID: 13

Txt: Sunday's earthquake was felt in the southern Indian city of Madras on the mainland, as well as other parts of south India. The Naval meteorological office in Port Blair said it was the second biggest aftershock after the Dec. 26 earthquake.

Hyp: The city of Madras is located in Southern India. (yes)

	The DT	city NN	Madras NNP	is VBZ	located VBN	Southern_India NNP
NO_WORD	0.96	0.15	0.04	1.57	0.17	0.07
Sunday:NNP	0.71	1.18	0.95	1.07	1.07	1.02
earthquake:NN	0.76	0.76	1.07	0.82	0.49	1.09
was:VBD	0.76	0.91	1.14	0.05	0.74	1.16
felt:VBN	0.01	0.91	1.16	0.67	0.60	1.16
the:DT	1.00	1.00	1.25	1.25	1.25	1.25
southern:JJ	1.02	0.98	1.05	1.22	0.72	0.17
Indian:JJ	0.71	1.13	0.97	1.22	1.22	1.05
city:NN	0.96	1.82	1.07	0.82	0.46	1.02
Madras:NNP	0.01	0.99	1.82	1.07	1.07	0.82
the:DT	0.76	1.00	1.25	1.25	1.25	1.25
mainland:JJ	0.65	0.87	1.07	0.96	0.73	1.13
other:JJ	0.71	0.88	1.13	0.96	0.92	1.12
parts:NNS	0.76	0.63	0.97	0.82	0.59	0.98
south:JJ	0.96	0.72	1.13	0.96	0.31	1.12
India:NNP	0.32	1.02	0.76	1.07	1.07	0.86
The:DT	0.96	1.00	1.25	1.25	1.25	1.25
Naval:NNP	0.76	1.07	0.93	1.07	1.07	0.99
meteorological:JJ	0.71	0.88	1.13	0.96	0.87	1.10
office:NN	0.96	0.40	1.07	0.82	0.54	1.09
Port_Blair:NNP	0.76	1.07	0.99	1.07	1.07	0.89
said:VBD	0.71	0.86	1.16	1.00	0.65	1.16
it:PRP	0.71	0.40	1.07	0.82	0.54	1.09
was:VBD	0.76	0.91	1.14	0.05	0.74	1.16
the:DT	0.01	1.00	1.25	1.25	1.25	1.25
second:JJ	1.02	1.13	1.05	1.22	1.17	1.05
biggest:JJS	0.76	0.82	1.12	0.96	0.84	1.13
aftershock:NN	0.71	0.71	1.07	0.82	0.71	1.04
the:DT	0.01	1.00	1.25	1.25	1.25	1.25
Dec.:NNP	0.96	1.20	0.99	1.07	1.07	1.03
26:CD	0.89	1.31	1.24	1.30	1.30	1.24
earthquake:NN	0.71	0.76	1.07	0.82	0.49	1.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.39 Alignment.score
1.00 0.45 Alignment.isGood
-1.00 0.52 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "south" modifying "India" is dropped on aligned hypothesis word "Southern_India"
1.00 1.00 Location.match :
-4.00 0.44 NullPunisher.other : located
-0.10 0.50 NullPunisher.functionWord : is
-2.00 1.00 RootEntailment.unalignedRoot : "located" not aligned to anything
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Madras"
1.00 1.00 WorldKnowledge.match : Location match: "India" entails "Southern_India"
4.00 1.00 Location.WorldKnowledge.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 5.1438
Threshold: -0.1576

Inference ID: 14

Txt: A powerful bomb, hidden in a truck, exploded outside the headquarters of Colombia's secret police, Wednesday, causing severe damage, Bogota mayor Andres Pastrana said.

Hyp: An attempt on Andres Pastrana's life was carried out using a powerful bomb. (don't know)

	An DT	attempt NN	Andres_Pastrana NNP	life NN	was VBD	carried VBN	using VBG	a DT	powerful JJ	bomb NN
A:DT	1.14	1.00	1.25	1.00	1.25	1.25	1.25	0.13	1.18	1.00
powerful:JJ	0.76	0.53	1.13	0.88	0.96	0.88	0.65	0.76	1.36	0.82
bomb:NN	0.71	0.76	1.07	0.86	0.82	0.56	0.63	0.71	0.93	1.82
hidden:VBN	0.76	0.75	1.16	0.68	0.82	0.50	0.37	0.76	0.73	0.63
a:DT	1.14	1.00	1.25	1.00	1.25	1.25	1.25	0.32	1.18	1.00
truck:NN	0.71	0.89	1.07	0.83	0.82	0.73	0.82	0.71	0.99	0.22
exploded:VBD	0.76	0.76	1.16	0.86	0.71	0.21	0.56	0.76	0.89	0.29
the:DT	1.31	1.00	1.25	0.96	1.25	1.25	1.25	1.31	1.18	1.00
headquarters:NN	0.71	0.85	1.07	0.87	0.82	0.74	0.66	0.71	0.91	0.43
Colombia:NNP	0.96	1.13	0.99	1.10	1.07	1.06	1.07	0.96	1.22	1.01
secret_police:NN	0.71	0.82	1.03	0.82	0.82	0.82	0.82	0.71	0.99	0.82
Wednesday:NNP	0.96	1.18	0.97	1.12	1.07	1.07	1.07	0.96	1.23	1.10
causing:VBG	0.76	0.65	1.16	0.91	0.96	0.42	0.97	0.76	0.78	0.64
severe:JJ	0.76	0.68	1.13	0.64	0.96	0.74	0.96	0.76	0.54	0.80
damage:NN	0.71	0.76	1.07	0.71	0.82	0.56	0.80	0.71	0.94	0.61
Bogota:NNP	0.96	1.11	0.99	1.08	1.07	1.07	1.07	0.96	1.24	0.99
mayor:NN	0.71	0.92	1.07	0.87	0.82	0.82	0.82	0.71	0.99	0.53
Andres_Pastrana:NNP	0.96	1.07	1.82	1.07	1.07	1.07	1.07	0.96	1.24	1.07
said:VBD	0.76	0.91	1.16	0.90	0.97	0.71	0.85	0.76	0.97	0.86
NO_WORD	0.82	0.15	0.12	0.02	1.57	0.17	0.16	0.82	0.11	0.09

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.07 Alignment.score
1.00 0.37 Alignment.isGood
-1.00 0.60 Alignment.isBad
-0.10 5.00 Alignment.numWordNotAligned
0.10 4.00 Alignment.hypSpan
0.10 0.30 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "life" modifying "attempt"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "hidden" modifying "bomb" is dropped on aligned hypothesis word "bomb"
-4.00 0.41 NullPunisher.other : carried
-0.10 1.00 NullPunisher.functionWord : An
-0.10 0.50 NullPunisher.functionWord : was
-4.00 0.32 NullPunisher.other : attempt
-4.00 0.16 NullPunisher.other : life
-2.00 1.00 RootEntailment.unalignedRoot : "carried" not aligned to anything
Hand-tuned score (dot product of above): -5.4163
Threshold: -0.1576

Inference ID: 18

Txt: Muslims make up some 3.2 million of Germany's 82 million people, and Turks represent two thirds of the minority.

Hyp: 82 million people live in Germany. (yes)

	82 CD	million CD	people NNS	live VBP	Germany NNP
Muslims:NNPS	1.29	1.20	0.93	0.80	1.08
make:VBP	1.39	1.39	0.61	0.10	1.14
up:IN	1.30	1.30	1.31	1.24	1.57
some:DT	1.18	1.18	0.95	1.20	1.25
3.2:CD	0.29	0.35	1.30	1.30	1.24
million:CD	0.22	1.25	1.23	1.28	1.24
Germany:NNP	1.20	1.20	1.19	1.07	1.82
82:CD	1.25	0.30	1.33	1.02	1.24
million:CD	0.30	1.25	1.23	1.28	1.24
people:NNS	1.29	1.19	1.82	0.44	1.19
Turks:NNPS	1.20	1.20	1.14	1.07	0.97
represent:VBP	1.31	1.39	0.72	0.63	1.11
two:CD	0.23	0.35	1.33	1.27	1.24
thirds:NNS	1.29	1.29	0.85	0.77	1.13
the:DT	1.18	1.18	1.00	1.22	1.25
minority:NN	1.20	1.22	0.42	0.81	1.11
NO_WORD	0.42	0.52	0.28	0.17	0.07

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.38 Alignment.score
1.00 0.45 Alignment.isGood
-1.00 0.52 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.80 Alignment.txtSpan
1.00 1.00 Location.match : compl "Germany" matches "Germany"
-4.00 0.33 NullPunisher.other : live
-2.00 1.00 RootEntailment.unalignedRoot : "live" not aligned to anything
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Germany"
4.00 1.00 Location.WorldKnowledge.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 4.2635
Threshold: -0.1576

Inference ID: 23

Txt: A.C. Milan is in second place with 13 points, after two first-half goals from captain Paolo Maldini earned them a 2-1 victory over Reggina.

Hyp: Paolo Maldini plays for Reggina. (don't know)

	Paolo_Maldini NNP	plays VBZ	Reggina NNP
NO_WORD	0.75	0.17	0.05
A.C._Milan:NNP	1.16	1.07	0.80
is:VBZ	1.05	0.91	1.16
second:JJ	1.07	0.99	0.99
place:NN	1.24	0.68	1.07
13:CD	1.07	1.24	1.24
points:NNS	1.57	0.55	1.06
after:IN	1.24	1.24	1.57
two:CD	1.12	1.30	1.24
first-half:JJ	1.07	0.96	1.13
goals:NNS	1.05	0.31	1.07
captain:NNP	1.68	0.57	1.04
Paolo_Maldini:NNP	1.16	1.07	0.95
earned:VBD	1.07	0.62	1.15
them:PRP	1.07	0.31	1.07
a:DT	1.25	1.25	1.25
2-1:JJ	1.13	0.96	1.13
victory:NN	1.07	0.62	1.07
Reggina:NNP	0.82	1.07	1.82

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.34 Alignment.score
1.00 0.44 Alignment.isGood
-1.00 0.53 Alignment.isBad
-0.10 0.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.67 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "captain" modifying "Paolo_Maldini" is dropped on aligned hypothesis word "Paolo_Maldini"
Hand-tuned score (dot product of above): 2.1204
Threshold: -0.1576

Inference ID: 25

Txt: On 12 August, the San Carlos Battalion came across mines placed in their path and one soldier was killed while two were seriously injured. Meanwhile on 10 August, urban commandos took a patrol car by surprise and dropped a grenade inside the car, injuring four and partially destroying the vehicle.

Hyp: Four people were injured by a grenade. (yes)

	Four CD	people NNS	were VBD	injured VBN	a DT	grenade NN
12:CD	0.45	1.33	1.30	1.23	0.89	1.32
August:NNP	1.20	1.21	1.07	1.06	0.96	1.08
the:DT	1.18	1.00	1.22	1.25	1.31	1.00
San_Carlos_Battalion:NNP	1.20	1.16	1.07	1.07	0.96	1.02
came:VBD	1.39	0.91	0.76	0.53	0.76	0.72
mines:NNS	1.29	0.85	0.79	0.74	0.71	0.65
placed:VBN	1.39	0.87	0.96	0.48	0.76	0.69
their:PRP$	1.43	1.30	0.95	0.97	1.11	1.30
path:NN	1.29	0.92	0.82	0.82	0.71	0.75
one:NN	1.26	0.64	0.78	0.82	0.71	0.82
soldier:NN	1.27	0.50	0.82	0.56	0.71	0.35
was:VBD	1.39	0.91	0.13	0.85	0.76	0.91
killed:VBN	1.39	0.51	0.85	0.03	0.76	0.47
while:IN	1.30	1.25	1.17	1.24	1.00	1.31
two:CD	0.35	1.33	1.30	1.24	0.89	1.30
were:VBD	1.39	0.86	0.78	0.83	0.76	0.89
seriously:RB	1.36	0.77	0.91	0.54	1.09	1.11
injured:VBN	1.37	0.65	0.83	0.78	0.76	0.61
Meanwhile:RB	1.36	1.17	0.91	0.91	1.09	1.19
10:CD	0.45	1.33	1.30	1.26	0.89	1.33
August:NNP	1.20	1.21	1.07	1.06	0.96	1.08
urban:JJ	1.13	0.67	0.96	0.96	0.76	0.84
commandos:NNS	1.29	0.82	0.82	0.52	0.71	0.27
took:VBD	1.34	0.91	0.86	0.43	0.76	0.75
a:DT	1.18	1.00	1.25	1.25	0.32	1.00
patrol_car:NN	1.29	0.87	0.82	0.82	0.71	0.70
surprise:NN	1.29	0.87	0.82	0.69	0.71	0.63
dropped:VBD	1.39	0.82	0.82	0.53	0.76	0.87
a:DT	1.18	1.00	1.25	1.25	0.32	1.00
grenade:NN	1.29	0.71	0.80	0.52	0.71	1.82
the:DT	1.18	1.00	1.22	1.25	1.31	1.00
car:NN	1.26	0.76	0.78	0.68	0.71	0.40
injuring:VBG	1.39	0.63	0.85	0.84	0.76	0.49
four:CD	1.27	1.33	1.30	1.25	0.89	1.29
partially:RB	1.36	1.20	0.91	0.78	1.09	1.09
destroying:VBG	1.39	0.64	0.87	0.44	0.76	0.60
the:DT	1.18	1.00	1.22	1.25	1.31	1.00
vehicle:NN	1.29	0.72	0.80	0.77	0.71	0.47
NO_WORD	0.52	0.15	1.57	0.17	0.82	0.21

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.28 Alignment.score
1.00 0.42 Alignment.isGood
-1.00 0.55 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 4.00 Alignment.hypSpan
0.10 0.67 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "seriously" modifying "injured" is dropped on aligned hypothesis word "injured"
-4.00 0.07 NullPunisher.other : people
Hand-tuned score (dot product of above): 1.7280
Threshold: -0.1576

Inference ID: 31

Txt: During that time, Wilson received the best-play Tony Award for "Fences," plus best-play Tony nominations for six of his other plays, the Pulitzer Prize for both "Fences" and "The Piano Lesson," and (a record) seven New York Drama Critics' Circle prizes.

Hyp: Wilson was awarded the Pulitzer Prize. (yes)

	Wilson NNP	was VBD	awarded VBN	the DT	Pulitzer_Prize NN
NO_WORD	1.25	1.57	0.17	0.82	0.09
that:DT	1.12	1.22	1.25	1.21	1.25
time:NN	1.82	0.82	0.81	0.61	1.10
Wilson:NNP	1.16	1.05	1.06	0.96	0.87
received:VBD	1.25	0.96	0.25	0.76	1.16
the:DT	1.13	1.25	1.25	0.32	1.25
best-play:JJ	0.83	0.96	0.96	0.76	1.13
Tony_Award:NN	0.94	1.07	1.03	0.96	0.95
Fences:NNP	1.25	1.07	1.06	0.96	0.78
plus:CC	1.13	1.22	1.03	1.31	1.25
best-play:JJ	0.82	0.96	0.96	0.76	1.13
Tony:NNP	1.07	1.07	1.07	0.93	0.95
nominations:NNS	1.24	0.82	0.67	0.71	1.07
six:CD	1.82	1.30	1.28	0.89	1.24
his:PRP$	1.82	1.05	1.06	0.96	0.87
other:JJ	1.13	0.96	0.96	0.65	1.13
plays:NNS	1.07	0.76	0.81	0.71	1.08
the:DT	1.25	1.25	1.25	0.32	1.25
Pulitzer_Prize:NN	0.87	1.07	1.07	0.96	1.82
both:DT	1.25	1.25	1.25	1.28	1.25
Fences:NNP	0.94	1.07	1.06	0.96	0.78
The:DT	1.25	1.25	1.25	0.01	1.25
Piano_Lesson:NNP	0.91	1.07	1.07	0.96	0.80
a:DT	1.25	1.25	1.25	1.31	1.25
record:NN	1.14	0.82	0.72	0.71	1.10
seven:CD	1.22	1.30	1.27	0.89	1.24
New_York_Drama_Critics:NNP	0.95	1.07	1.07	0.96	0.82
Circle:NN	1.05	0.82	0.80	0.71	1.07
prizes:NNS	1.10	0.82	0.62	0.71	1.08

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.31 Alignment.score
1.00 0.43 Alignment.isGood
-1.00 0.54 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.40 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Fences" modifying "Pulitzer_Prize" is dropped on aligned hypothesis word "Pulitzer_Prize"
-0.10 0.50 NullPunisher.functionWord : was
Hand-tuned score (dot product of above): 1.8994
Threshold: -0.1576

Inference ID: 33

Txt: Muslims make up some 3.2 million of Germany's 82 million people, and Turks represent two thirds of the minority.

Hyp: 3.2 million Muslims live in Germany. (yes)

	3.2 CD	million CD	Muslims NNPS	live VBP	Germany NNP
Muslims:NNPS	1.20	1.11	1.82	1.05	0.99
make:VBP	1.39	1.39	1.16	0.10	1.14
up:IN	1.30	1.30	1.57	1.24	1.57
some:DT	1.18	1.18	1.23	1.20	1.25
3.2:CD	1.25	0.43	1.24	1.30	1.24
million:CD	0.43	1.25	1.14	1.28	1.24
Germany:NNP	1.20	1.20	0.99	1.07	1.82
82:CD	0.29	0.22	1.24	1.02	1.24
million:CD	0.35	1.25	1.14	1.28	1.24
people:NNS	1.27	1.19	1.19	0.44	1.19
Turks:NNPS	1.20	1.20	0.71	1.07	0.97
represent:VBP	1.29	1.39	1.16	0.63	1.11
two:CD	0.35	0.35	1.24	1.27	1.24
thirds:NNS	1.29	1.29	1.10	0.77	1.13
the:DT	1.18	1.18	1.25	1.22	1.25
minority:NN	1.22	1.22	1.06	0.81	1.11
NO_WORD	0.42	0.52	0.28	0.17	0.07

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.27 Alignment.score
1.00 0.42 Alignment.isGood
-1.00 0.55 Alignment.isBad
-0.10 0.00 Alignment.numWordNotAligned
0.10 5.00 Alignment.hypSpan
0.10 0.60 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "people" modifying "million" is dropped on aligned hypothesis word "million"
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Germany"
Hand-tuned score (dot product of above): 3.1977
Threshold: -0.1576

Inference ID: 34

Txt: "The capacity people have to adjust and make peace with a new situation, even if they feel vulnerable, is really remarkable," said Randy Quevillon, chair of the psychology department and the Disaster Mental Health Institute at the University of South Dakota.

Hyp: Randy Quevillon is employed at the University of South Dakota. (yes)

	Randy_Quevillon NNP	is VBZ	employed VBN	the DT	University_of_South_Dakota NNP
NO_WORD	1.25	1.57	0.17	0.82	0.20
The:DT	1.07	1.25	1.25	0.01	1.25
capacity:NN	1.07	0.82	0.62	0.71	1.11
people:NNS	1.16	0.82	0.49	0.71	1.11
have:VBP	1.25	0.98	0.87	0.73	1.16
to:TO	1.16	1.25	1.25	1.26	1.25
adjust:VB	1.15	0.87	0.76	0.76	1.16
make_peace:VB	1.57	0.56	0.54	0.76	1.16
with:IN	1.25	1.24	1.24	1.01	1.57
a:DT	1.13	1.25	1.25	1.31	1.25
new:JJ	1.06	0.96	0.91	0.76	1.13
situation:NN	1.47	0.82	0.64	0.71	1.17
even:RB	1.57	0.91	0.91	1.06	1.47
if:IN	1.07	1.13	1.24	1.16	1.57
they:PRP	1.07	0.82	0.49	0.71	1.11
feel:VBP	1.16	0.79	0.64	0.73	1.16
vulnerable:JJ	1.13	0.96	0.85	0.76	1.13
is:VBZ	1.16	0.78	0.93	0.76	1.16
really:RB	1.47	0.91	0.88	1.09	1.47
remarkable:JJ	1.13	0.96	0.83	0.76	1.13
said:VBD	1.16	1.00	0.76	0.76	1.16
Randy_Quevillon:NNP	1.82	1.07	1.07	0.96	0.95
chair:NN	1.07	0.82	0.82	0.71	1.11
the:DT	1.25	1.25	1.25	0.32	1.25
psychology_department:NN	1.07	0.82	0.82	0.71	1.06
the:DT	1.25	1.25	1.25	0.32	1.25
Disaster_Mental_Health_Institute:NN	0.95	1.07	1.07	0.96	0.83
the:DT	1.25	1.25	1.25	0.32	1.25
University_of_South_Dakota:NNP	0.95	1.07	1.07	0.96	1.82

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.29 Alignment.score
1.00 0.43 Alignment.isGood
-1.00 0.54 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.40 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Disaster_Mental_Health_Institute" modifying "Randy_Quevillon" is dropped on aligned hypothesis word "Randy_Quevillon"
-0.10 0.50 NullPunisher.functionWord : is
-4.00 0.46 NullPunisher.other : employed
-2.00 1.00 RootEntailment.unalignedRoot : "employed" not aligned to anything
Hand-tuned score (dot product of above): -2.1759
Threshold: -0.1576

Inference ID: 35

Hyp: Ahmadinejad attacked the UN Security Council. (don't know)

	Ahmadinejad NNP	attacked VBD	the DT	UN_Security_Council NNP
Meanwhile:RB	1.21	0.90	1.09	1.47
an:DT	1.00	1.25	1.31	1.25
exclusive:JJ	0.88	0.91	0.76	1.13
interview:NN	0.82	0.67	0.71	1.08
a:DT	1.00	1.25	1.31	1.25
TIME:JJ	0.88	0.96	0.67	1.13
journalist:NN	0.82	0.65	0.71	1.11
the:DT	1.00	1.25	0.32	1.25
first:JJ	1.13	1.22	1.02	1.05
one-on-one:JJ	0.87	0.96	0.76	1.13
session:NN	0.82	0.80	0.71	1.09
given:VBN	0.91	0.55	0.76	1.16
to:TO	1.00	1.25	1.26	1.25
a:DT	1.00	1.25	1.31	1.25
Western:JJ	1.13	1.22	1.02	1.05
print:NN	0.82	0.82	0.71	1.07
publication:NN	0.82	0.82	0.71	1.14
his:PRP$	1.30	0.97	1.11	1.55
election:NN	0.82	0.55	0.71	1.09
president:NN	0.80	0.55	0.71	1.13
Iran:NNP	1.07	1.07	0.96	1.00
earlier:RBR	1.21	0.87	1.09	1.47
this:DT	1.00	1.25	1.21	1.25
year:NN	0.82	0.82	0.71	1.12
Ahmadinejad:NNP	1.82	0.76	0.71	1.07
attacked:VBD	0.85	0.78	0.76	1.16
the:DT	1.00	1.25	0.32	1.25
threat:NN	0.80	0.64	0.63	1.09
to:TO	1.00	1.25	1.26	1.25
bring:VB	0.91	0.58	0.76	1.16
the:DT	1.00	1.25	0.32	1.25
issue:NN	0.82	0.72	0.71	1.11
Iran:NNP	1.07	1.07	0.96	1.00
nuclear:JJ	0.88	0.94	0.76	1.13
activity:NN	0.81	0.76	0.71	1.18
the:DT	1.00	1.25	0.32	1.25
UN_Security_Council:NNP	1.07	1.07	0.96	1.82
the:DT	1.00	1.25	0.32	1.25
US:NNP	1.07	1.07	0.96	1.05
France:NNP	1.07	1.04	0.96	1.01
Britain:NNP	1.07	1.06	0.96	1.02
Germany:NNP	1.07	1.07	0.96	1.01
NO_WORD	0.28	0.17	0.82	0.09

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.55 Alignment.score
1.00 0.49 Alignment.isGood
-1.00 0.48 Alignment.isBad
-0.10 0.00 Alignment.numWordNotAligned
0.10 4.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "interview" modifying "attacked" is dropped on aligned hypothesis word "attacked"
Hand-tuned score (dot product of above): 2.5206
Threshold: -0.1576

Inference ID: 41

Txt: Arsenal sneaked a 1-0 victory over Birmingham at Highbury, taking advantage of Stephen Clemence's own-goal in the 81st minute.

Hyp: Arsenal lost to Birmingham. (don't know)

	Arsenal NNP	lost VBD	Birmingham NNP
Arsenal:NNP	1.82	1.07	0.91
sneaked:VBD	1.16	0.57	1.16
a:DT	1.25	1.25	1.25
1-0:JJ	1.13	0.74	1.13
victory:NN	1.10	0.50	1.10
Birmingham:NNP	0.91	1.07	1.82
Highbury:NNP	0.99	1.07	0.82
taking:VBG	1.15	0.51	1.14
advantage:NN	1.07	0.75	1.11
Stephen_Clemence:NNP	0.92	1.07	0.96
own-goal:NN	1.03	0.82	1.05
the:DT	1.25	1.25	1.25
81st:JJ	1.05	1.11	1.05
minute:NN	1.08	0.82	1.05
NO_WORD	0.28	0.17	0.74

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.52 Alignment.score
1.00 0.48 Alignment.isGood
-1.00 0.49 Alignment.isBad
-0.10 0.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.67 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "1-0" modifying "victory" is dropped on aligned hypothesis word "lost"
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Birmingham"
Hand-tuned score (dot product of above): 3.3868
Threshold: -0.1576

Inference ID: 44

Hyp: A patrol car was attacked by the San Carlos Battalion. (don't know)

	A DT	patrol_car NN	was VBD	attacked VBN	the DT	San_Carlos_Battalion NNP
12:CD	0.89	1.33	1.30	1.30	0.89	1.24
August:NNP	0.96	1.09	1.07	1.07	0.96	1.02
the:DT	1.31	1.00	1.25	1.25	0.32	1.25
San_Carlos_Battalion:NNP	0.96	1.03	1.07	1.07	0.96	1.82
came:VBD	0.76	0.91	0.78	0.42	0.73	1.16
mines:NNS	0.71	0.74	0.82	0.78	0.71	1.05
placed:VBN	0.76	0.88	0.96	0.41	0.76	1.16
their:PRP$	1.11	1.30	0.97	0.97	1.00	1.55
path:NN	0.71	0.83	0.78	0.81	0.67	1.12
one:NN	0.71	0.83	0.82	0.82	0.63	1.11
soldier:NN	0.71	0.75	0.82	0.50	0.71	1.03
was:VBD	0.76	0.91	0.78	0.82	0.76	1.16
killed:VBN	0.76	0.91	0.85	0.11	0.76	1.16
while:IN	1.00	1.31	1.24	1.24	0.99	1.57
two:CD	0.89	1.33	1.30	1.25	0.82	1.24
were:VBD	0.76	0.91	0.13	0.82	0.73	1.16
seriously:RB	1.09	1.20	0.91	0.61	1.09	1.47
injured:VBN	0.76	0.91	0.85	0.23	0.76	1.16
Meanwhile:RB	1.09	1.21	0.91	0.90	1.09	1.47
10:CD	0.89	1.33	1.30	1.30	0.89	1.24
August:NNP	0.96	1.09	1.07	1.07	0.96	1.02
urban:JJ	0.76	0.88	0.96	0.96	0.76	1.13
commandos:NNS	0.71	0.77	0.82	0.39	0.71	1.00
took:VBD	0.76	0.91	0.86	0.31	0.73	1.16
a:DT	0.13	1.00	1.25	1.25	1.31	1.25
patrol_car:NN	0.71	1.82	0.82	0.79	0.71	1.03
surprise:NN	0.71	0.82	0.82	0.68	0.71	1.09
dropped:VBD	0.76	0.89	0.82	0.46	0.76	1.16
a:DT	0.13	1.00	1.25	1.25	1.31	1.25
grenade:NN	0.71	0.70	0.82	0.36	0.71	1.02
the:DT	1.31	1.00	1.25	1.25	0.32	1.25
car:NN	0.71	0.63	0.74	0.64	0.71	1.08
injuring:VBG	0.76	0.91	0.85	0.22	0.76	1.16
four:CD	0.89	1.33	1.30	1.29	0.89	1.24
partially:RB	1.09	1.16	0.91	0.69	1.09	1.47
destroying:VBG	0.76	0.86	0.87	0.41	0.76	1.16
the:DT	1.31	1.00	1.25	1.25	0.32	1.25
vehicle:NN	0.71	0.66	0.82	0.71	0.71	1.09
NO_WORD	0.82	0.15	1.57	0.17	0.82	0.21

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.45 Alignment.score
1.00 0.47 Alignment.isGood
-1.00 0.50 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.83 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "surprise" modifying "took" is dropped on aligned hypothesis word "attacked"
-3.00 1.00 Structure.clearBadness : passive struct without correct agent (commandos vs. San_Carlos_Battalion)
-0.10 0.50 NullPunisher.functionWord : was
Hand-tuned score (dot product of above): -0.8570
Threshold: -0.1576

Inference ID: 46

Txt: Bogota, 4 May 88 - The dissemination of a document questioning Colombia's oil policy, is reportedly the aim of the publicity stunt carried out by the pro-Castro Army Of National Liberation, which kidnapped several honorary consuls, newsmen, and political leaders.

Hyp: Several honorary consuls were kidnapped on 4 May 88. (don't know)

	Several JJ	honorary JJ	consuls NNS	were VBD	kidnapped VBN	4 CD	May NNP	88 CD
Bogota:NNP	1.23	1.18	1.01	1.07	1.07	1.20	0.99	1.20
4:CD	1.00	1.00	1.33	1.30	1.30	1.25	1.07	0.16
May:NNP	1.24	1.24	1.08	1.07	1.07	1.03	1.82	1.03
88:CD	1.00	0.96	1.33	1.30	1.28	0.16	1.07	1.25
The:DT	1.18	1.18	1.00	1.22	1.25	1.18	1.25	1.18
dissemination:NN	0.99	0.99	0.82	0.82	0.80	1.29	1.10	1.29
a:DT	1.18	1.18	1.00	1.25	1.25	1.18	1.25	1.18
document:NN	0.99	0.95	0.85	0.82	0.80	1.28	1.20	1.29
questioning:VBG	1.02	1.02	0.91	0.75	0.32	1.37	1.16	1.38
Colombia:NNP	1.24	1.19	0.99	1.07	1.07	1.20	1.01	1.20
oil:NN	0.99	0.90	0.80	0.82	0.76	1.27	1.13	1.19
policy:NN	0.99	0.76	0.81	0.82	0.82	1.29	1.12	1.29
is:VBZ	1.02	1.02	0.91	0.10	0.72	1.39	1.16	1.39
reportedly:RB	1.01	0.93	1.20	0.91	0.54	1.36	1.47	1.30
the:DT	1.18	1.18	1.00	1.22	1.25	1.18	1.25	1.18
aim:NN	0.99	0.99	0.83	0.82	0.82	1.24	1.13	1.29
the:DT	1.18	1.18	1.00	1.22	1.25	1.18	1.25	1.18
publicity:NN	0.99	0.97	0.84	0.82	0.67	1.29	1.14	1.29
stunt:NN	0.99	0.99	0.82	0.82	0.65	1.27	1.10	1.20
carried_out:VBN	1.02	1.02	0.91	0.91	0.65	1.39	1.16	1.39
the:DT	1.18	1.18	1.00	1.22	1.25	1.18	1.25	1.18
pro-Castro_Army_Of_National_Liberation:NN	1.24	1.24	0.99	1.07	1.07	1.20	1.02	1.20
which:WDT	1.18	1.18	1.00	1.23	1.25	1.18	1.25	1.18
kidnapped:VBD	1.02	0.97	0.91	0.72	1.50	1.39	1.16	1.37
several:JJ	1.26	0.89	0.88	0.94	0.96	1.13	1.13	1.13
honorary:JJ	0.89	1.36	0.83	0.96	0.91	1.13	1.13	1.08
consuls:NNS	0.99	0.94	1.82	0.82	0.82	1.29	1.08	1.29
newsmen:NN	0.95	0.99	0.70	0.80	0.82	1.29	1.11	1.29
political_leaders:NNS	0.99	0.99	0.76	0.82	0.82	1.29	1.16	1.29
NO_WORD	0.11	0.11	0.15	1.57	0.17	0.52	0.09	0.52

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.86 Alignment.score
1.00 0.57 Alignment.isGood
-1.00 0.40 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 4.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
0.50 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 05/04/1988
0.50 1.00 Date.matchDatesByNormForm : hyp/txt matching, by normalized form: 05/04/1988
-0.10 0.50 NullPunisher.functionWord : were
Hand-tuned score (dot product of above): 3.3282
Threshold: -0.1576

Inference ID: 53

Txt: The FMLN has prudently taken time before adopting a definite position, meanwhile the government has increased its detentions and attacked the residences of leaders of the Social Christian People's Movement.

Hyp: Social Christian People's Movement was attacked by FMLN. (don't know)

	Social NNP	Christian NNP	People_'s_Movement NNP	was VBD	attacked VBN	FMLN NNP
NO_WORD	1.25	0.05	0.15	1.57	0.17	0.21
The:DT	0.99	1.25	1.25	1.25	1.25	1.25
FMLN:NN	1.16	0.99	0.82	1.07	1.07	1.82
has:VBZ	1.47	1.16	1.16	0.84	0.77	1.16
prudently:RB	1.16	1.47	1.47	0.91	0.83	1.47
taken:VBN	1.12	1.16	1.16	0.86	0.22	1.14
time:NN	1.13	1.20	1.23	0.82	0.78	1.07
adopting:VBG	1.25	1.12	1.16	0.83	0.65	1.16
a:DT	1.13	1.25	1.25	1.25	1.25	1.25
definite:JJ	1.11	1.09	1.13	0.96	0.96	1.13
position:NN	1.47	1.07	1.27	0.82	0.79	1.07
meanwhile:RB	1.25	1.45	1.47	0.91	0.90	1.47
the:DT	1.06	1.25	1.25	1.25	1.25	1.25
government:NN	1.16	1.24	1.12	0.82	0.69	1.07
has:VBZ	1.15	1.16	1.16	0.84	0.77	1.16
increased:VBN	0.99	1.16	1.16	0.95	0.69	1.16
its:PRP$	0.99	0.99	0.82	1.07	1.07	1.82
detentions:NNS	1.08	1.08	1.15	0.82	0.56	1.07
attacked:VBN	1.16	1.16	1.16	0.82	0.78	1.16
the:DT	1.25	1.25	1.25	1.25	1.25	1.25
residences:NNS	1.12	1.14	1.24	0.82	0.82	1.07
leaders:NNS	1.02	1.17	1.11	0.82	0.52	1.07
the:DT	1.25	1.25	1.25	1.25	1.25	1.25
Social_Christian_People_'s_Movement:NNP	0.44	0.07	0.37	1.07	1.07	0.82

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.74 Alignment.score
1.00 0.30 Alignment.isGood
-1.00 0.67 Alignment.isBad
-0.10 3.00 Alignment.numWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.17 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Christian" modifying "People_'s_Movement"
1.00 1.00 Factive.inPositiveEmbedding : embedded positive text
-0.10 0.50 NullPunisher.functionWord : was
-3.00 1.00 NullPunisher.entity : Social
-3.00 1.00 NullPunisher.entity : Christian
Hand-tuned score (dot product of above): -5.7711
Threshold: -0.1576

Inference ID: 58

Txt: "A force majeure is an act of God," said attorney Phil Wittmann, who represents the New Orleans Saints and owner Tom Benson's local interests.

Hyp: Phil Wittmann works for Tom Benson. (yes)

	Phil_Wittmann NNP	works VBZ	Tom_Benson NNP
A:DT	1.25	1.25	1.25
force_majeure:NN	1.07	0.82	1.06
is:VBZ	1.16	0.85	1.16
an:DT	1.25	1.25	1.25
act:NN	1.07	0.76	1.13
God:NNP	0.82	1.07	0.87
said:VBD	1.16	0.82	1.16
attorney:NN	1.07	0.80	1.04
Phil_Wittmann:NNP	1.82	1.07	0.81
who:WP	1.55	0.97	1.55
represents:VBZ	1.16	0.66	1.14
the:DT	1.25	1.25	1.25
New_Orleans:NNP	0.95	1.05	0.96
Saints:NNP	0.99	1.05	1.05
owner:NN	1.07	0.73	1.04
Tom_Benson:NNP	0.81	1.07	1.82
local:JJ	1.13	0.91	1.13
interests:NNS	1.07	0.79	1.12
NO_WORD	0.28	0.17	0.05

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.27 Alignment.score
1.00 0.42 Alignment.isGood
-1.00 0.55 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "owner" modifying "Tom_Benson" is dropped on aligned hypothesis word "Tom_Benson"
-4.00 0.30 NullPunisher.other : works
-2.00 1.00 RootEntailment.unalignedRoot : "works" not aligned to anything
Hand-tuned score (dot product of above): -1.5258
Threshold: -0.1576

Inference ID: 60

Txt: Co-producing the film is Happy Madison, the company owned by Sandler, who has been a compatriot of Schneider's since they met early in their careers, as mutually struggling stand-up comedians in Los Angeles.

Hyp: Happy Madison works for the company owned by Sandler. (don't know)

	Happy_Madison NNP	works VBZ	the DT	company NN	owned VBN	Sandler NNP
NO_WORD	1.11	0.17	0.82	0.05	0.16	0.30
Co-producing:JJ	1.25	0.96	0.76	0.84	0.96	1.13
the:DT	1.07	1.25	0.32	1.00	1.25	1.25
film:NN	1.16	0.44	0.71	0.68	0.75	1.07
is:VBZ	1.82	0.85	0.76	0.91	0.96	1.16
Happy_Madison:NNP	1.25	1.07	0.96	1.15	1.07	0.99
the:DT	1.15	1.25	0.32	1.00	1.25	1.25
company:NN	1.16	0.82	0.95	1.82	0.73	1.07
owned:VBN	0.99	0.63	0.76	0.82	0.78	1.13
Sandler:NNP	1.55	1.07	0.96	1.07	1.04	1.82
who:WP	1.16	0.97	1.04	1.30	0.97	1.55
has:VBZ	1.16	0.80	0.76	0.91	0.50	1.16
been:VBN	1.25	0.85	0.73	0.91	0.94	1.16
a:DT	0.96	1.25	1.31	1.00	1.25	1.25
compatriot:NN	0.99	0.69	0.71	0.34	0.70	1.07
Schneider:NNP	1.57	1.07	0.96	1.07	1.04	0.73
since:IN	0.99	1.24	1.05	1.31	1.20	1.50
they:PRP	0.99	1.07	0.96	1.07	1.04	0.73
met:VBD	1.16	0.66	0.76	0.91	0.77	1.16
early:RB	1.47	0.85	1.09	1.14	0.91	1.44
their:PRP$	0.99	1.07	0.96	1.07	1.04	0.73
careers:NNS	1.10	0.70	0.71	0.88	0.81	1.01
mutually:RB	1.47	0.80	1.09	1.06	0.86	1.46
struggling:VBG	1.16	0.70	0.76	0.91	0.68	1.15
stand-up:JJ	1.13	0.96	0.76	0.88	0.96	1.03
comedians:NNS	1.01	0.52	0.71	0.86	0.81	1.07
Los_Angeles:NNP	0.82	1.07	0.96	1.05	1.07	0.91

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.44 Alignment.score
1.00 0.46 Alignment.isGood
-1.00 0.51 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 4.00 Alignment.hypSpan
0.10 0.83 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "been" modifying "Sandler" is dropped on aligned hypothesis word "Sandler"
3.00 1.00 Apposition.match : Happy_Madison in relation appos with "company"
-4.00 0.30 NullPunisher.other : works
-2.00 1.00 RootEntailment.unalignedRoot : "works" not aligned to anything
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Happy_Madison"
4.00 1.00 Location.WorldKnowledge.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 7.0785
Threshold: -0.1576

Inference ID: 61

Txt: Although they were born on different planets, Oscar-winning actor Nicolas Cage's new son and Superman have something in common - both were named Kal-el.

Hyp: Nicolas Cage's son is called Kal-el. (yes)

	Nicolas_Cage NNP	son NN	is VBZ	called VBN	Kal-el NN
Although:IN	1.57	1.31	1.24	1.24	1.31
they:PRP	1.55	1.30	0.97	0.97	1.30
were:VBD	1.16	0.91	0.10	0.86	0.91
born:VBN	1.16	0.27	0.80	0.61	0.75
different:JJ	1.13	0.88	0.96	0.95	0.88
planets:NNS	1.05	0.84	0.82	0.80	0.80
Oscar-winning:NNP	1.06	0.83	0.82	0.82	0.81
actor:NNP	1.06	0.38	0.82	0.77	0.72
Nicolas_Cage:NNP	1.82	1.04	1.07	1.07	1.08
new:JJ	1.13	0.88	0.96	0.90	0.88
son:NN	1.04	1.82	0.82	0.73	0.76
Superman:NNP	0.93	1.00	1.07	1.07	1.07
have:VBP	1.16	0.91	0.98	0.76	0.86
something:NN	1.06	0.82	0.82	0.82	0.82
common:JJ	1.13	0.85	0.96	0.91	0.80
both:DT	1.25	0.96	1.25	1.25	1.00
were:VBD	1.16	0.91	0.10	0.86	0.91
named:VBN	1.16	0.78	0.86	0.19	0.80
Kal-el:NN	1.08	0.76	0.82	0.61	1.82
NO_WORD	0.12	0.15	1.57	0.17	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.90 Alignment.score
1.00 0.58 Alignment.isGood
-1.00 0.39 Alignment.isBad
-0.10 0.00 Alignment.numWordNotAligned
0.10 5.00 Alignment.hypSpan
0.10 0.60 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "new" modifying "son" is dropped on aligned hypothesis word "son"
1.00 1.00 Factive.factivePassage : Valid pattern: "have" an action X entails X
0.50 1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 4.6445
Threshold: -0.1576

Inference ID: 63

Txt: With Washington hinting it might soften its tough stance to break the deadlock, top US negotiator, Christopher Hill, said the two sides needed to review the situation.

Hyp: Christopher Hill works for the US. (yes)

	Christopher_Hill NNP	works VBZ	the DT	US NNP
NO_WORD	0.98	0.17	0.82	0.05
Washington:NNP	1.16	1.07	0.96	0.94
hinting:VBG	0.98	0.68	0.76	1.16
it:PRP	0.98	1.07	0.96	0.94
might:MD	1.25	1.25	1.31	1.25
soften:VB	1.16	0.62	0.76	1.16
its:PRP$	0.98	1.07	0.96	0.94
tough:JJ	1.13	0.91	0.76	1.13
stance:NN	1.09	0.82	0.68	1.17
to:TO	1.25	1.25	1.26	1.25
break:VB	1.16	0.59	0.76	1.16
the:DT	1.25	1.25	0.32	1.25
deadlock:NN	1.07	0.82	0.71	1.13
top:JJ	1.13	0.96	0.69	1.13
US:NNP	0.99	1.07	0.96	1.82
negotiator:NN	1.07	0.82	0.71	1.20
Christopher_Hill:NNP	1.82	1.07	0.96	0.99
said:VBD	1.16	0.82	0.76	1.16
the:DT	1.25	1.25	0.32	1.25
two:CD	1.24	1.30	0.82	1.24
sides:NNS	1.07	0.73	0.71	1.05
needed:VBD	1.16	0.57	0.76	1.16
to:TO	1.25	1.25	1.26	1.25
review:VB	1.16	0.47	0.76	1.16
the:DT	1.25	1.25	0.32	1.25
situation:NN	1.13	0.82	0.71	1.25

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.16 Alignment.score
1.00 0.39 Alignment.isGood
-1.00 0.58 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.25 Alignment.txtSpan
3.00 1.00 Apposition.match : Christopher_Hill in relation with "US"
-0.10 1.00 NullPunisher.functionWord : the
-4.00 0.30 NullPunisher.other : works
-2.00 1.00 RootEntailment.unalignedRoot : "works" not aligned to anything
Hand-tuned score (dot product of above): 0.5961
Threshold: -0.1576

Inference ID: 75

Txt: Jean-Claude Trichet, the European Central Bank president, made it clear, on Wednesday, that he would oppose unwarranted political attempts to remove Antonio Fazio: the Bank of Italy governor, engulfed in controversy over his handling of bank takeover bids.

Hyp: Antonio Fazio works for the Bank of Italy. (yes)

	Antonio_Fazio NNP	works VBZ	the DT	Bank_of_Italy NNP
NO_WORD	0.82	0.17	0.82	0.05
Jean-Claude_Trichet:NN	1.25	1.07	0.96	0.95
the:DT	0.95	1.25	0.32	1.25
European_Central_Bank:NNP	1.07	1.07	0.96	0.82
president:NN	1.16	0.82	0.71	1.15
made:VBD	1.55	0.16	0.73	1.16
it:PRP	1.13	0.97	1.11	1.55
clear:JJ	0.99	0.91	0.76	1.13
Wednesday:NNP	1.57	1.07	0.96	1.07
that:IN	0.82	1.24	0.95	1.57
he:PRP	0.82	1.07	0.96	0.95
would:MD	1.25	1.17	1.31	1.25
oppose:VB	1.16	0.69	0.76	1.16
unwarranted:JJ	1.13	0.96	0.76	1.12
political:JJ	1.11	0.92	0.76	1.11
attempts:NNS	1.07	0.69	0.71	1.24
to:TO	1.25	1.25	1.26	1.25
remove:VB	1.16	0.74	0.76	1.16
Antonio_Fazio:NNP	1.82	1.07	0.96	0.92
the:DT	1.25	1.25	0.32	1.25
Bank_of_Italy:NNP	0.92	1.07	0.96	1.82
governor:NN	1.07	0.80	0.71	1.10
engulfed:VBN	1.16	0.67	0.76	1.16
controversy:NN	1.06	0.73	0.71	1.12
his:PRP$	0.82	1.07	0.96	0.95
handling:NN	1.07	0.66	0.71	1.11
bank:NN	0.95	1.05	0.96	0.27
takeover_bids:NNS	1.04	0.82	0.71	1.04

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.16 Alignment.score
1.00 0.39 Alignment.isGood
-1.00 0.58 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.25 Alignment.txtSpan
3.00 1.00 Apposition.match : Antonio_Fazio in relation with "Bank_of_Italy"
-4.00 0.30 NullPunisher.other : works
-0.10 1.00 NullPunisher.functionWord : the
-2.00 1.00 RootEntailment.unalignedRoot : "works" not aligned to anything
Hand-tuned score (dot product of above): 0.5961
Threshold: -0.1576

Inference ID: 76

Txt: The assassins of former Defense Minister Enrique Lopez Albujar Trint are presumably members of the Maoist terrorist organization, Shining Path, since the counterterrorism division has identified one of them as Gerardo Olivos Silva, through a composite made from witness' reports.

Hyp: Gerardo Olivos Silva is a member of the Maoist terrorist organization Shining Path. (yes)

	Gerardo_Olivos_Silva NNP	is VBZ	a DT	member NN	the DT	Maoist NNP	terrorist_organization NN	Shining_Path NN
NO_WORD	1.25	1.43	0.82	0.12	0.82	0.05	0.05	0.04
The:DT	1.03	1.25	1.31	1.00	0.01	1.25	1.00	1.25
assassins:NNS	1.13	0.82	0.71	0.78	0.71	1.06	0.84	1.08
former:JJ	1.07	0.96	0.76	0.80	0.76	1.13	0.88	1.13
Defense:NNP	1.04	0.82	0.71	0.90	0.71	1.06	0.87	1.10
Minister:NNP	0.80	0.82	0.71	0.75	0.71	0.98	0.87	1.08
Enrique_Lopez_Albujar_Trint:NNP	1.16	1.07	0.96	1.07	0.96	0.99	1.04	0.95
are:VBP	1.47	0.02	0.76	0.91	0.68	1.16	0.91	1.16
presumably:RB	1.06	0.91	1.09	1.17	1.09	1.47	1.21	1.47
members:NNS	1.25	0.82	0.77	1.41	0.77	1.06	0.91	1.14
the:DT	1.05	1.25	1.31	1.00	0.32	1.25	1.00	1.25
Maoist:JJ	0.96	1.22	1.02	1.10	1.02	1.60	1.13	1.05
terrorist_organization:NN	0.88	0.82	0.71	0.91	0.71	1.07	1.82	0.91
Shining_Path:NNP	1.57	1.07	0.96	1.14	0.96	0.99	0.83	2.22
since:IN	1.25	1.24	1.05	1.29	1.05	1.57	1.31	1.57
the:DT	1.05	1.25	1.31	1.00	0.32	1.25	1.00	1.25
counterterrorism:NN	1.01	0.82	0.71	0.85	0.71	1.07	0.81	1.06
division:NN	1.16	0.82	0.71	0.85	0.71	1.04	0.79	1.02
has:VBZ	1.16	1.01	0.76	0.91	0.76	1.14	0.91	1.16
identified:VBN	1.24	0.86	0.76	0.74	0.76	1.16	0.91	1.14
one:CD	0.80	1.30	0.89	1.33	0.82	1.24	1.33	1.24
them:PRP	0.80	1.07	0.96	1.07	0.96	0.99	1.04	0.95
Gerardo_Olivos_Silva:NNP	1.82	1.07	0.96	1.06	0.96	0.99	0.96	0.88
a:DT	1.25	1.25	0.32	1.00	1.31	1.25	1.00	1.25
composite:NN	1.07	0.82	0.71	0.90	0.71	1.03	0.86	1.09
made:VBN	1.16	0.84	0.76	0.86	0.73	1.12	0.91	1.16
witness:NN	1.04	0.82	0.71	0.57	0.71	1.06	0.86	1.09
reports:NNS	1.08	0.82	0.71	0.92	0.71	1.06	0.88	1.11

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.32 Alignment.score
1.00 0.43 Alignment.isGood
-1.00 0.54 Alignment.isBad
-0.10 3.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.38 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "presumably" modifying "members" is dropped on aligned hypothesis word "member"
-0.10 1.00 NullPunisher.functionWord : a
-0.10 0.50 NullPunisher.functionWord : is
-0.10 1.00 NullPunisher.functionWord : the
Hand-tuned score (dot product of above): 1.5050
Threshold: -0.1576

Inference ID: 85

Txt: Linda Johnson, who lives with her husband, Charles, and two cats in a rented one-bedroom apartment in San Francisco's Mission District, said Katrina has made her realize she needs to gather together, in one accessible place, her scattered disaster supplies.

Hyp: Linda Johnson is married to Charles. (yes)

	Linda_Johnson NNP	is VBZ	married VBN	Charles NNP
NO_WORD	2.22	1.57	0.17	0.74
Linda_Johnson:NN	1.55	1.07	1.07	0.77
who:WP	1.16	0.97	0.97	1.55
lives:VBZ	1.55	0.28	0.20	1.13
her:PRP$	1.02	0.97	0.97	1.55
husband:NN	0.77	0.82	0.09	0.86
Charles:NNP	1.24	1.07	1.01	1.82
two:CD	1.03	1.30	1.03	1.24
cats:NNS	1.25	0.82	0.67	0.97
a:DT	1.16	1.25	1.25	1.25
rented:VBN	1.12	0.74	0.34	1.15
one-bedroom:JJ	1.07	0.96	0.96	1.13
apartment:NN	0.95	0.82	0.41	1.02
San_Francisco:NNP	0.97	1.07	1.07	0.99
Mission_District:NNP	1.16	1.07	1.07	0.98
said:VBD	0.82	1.00	0.63	1.16
Katrina:NNP	1.16	1.07	0.98	0.79
has:VBZ	1.16	1.01	0.68	1.12
made:VBN	0.82	0.84	0.59	1.14
her:PRP	0.82	1.07	0.98	0.79
realize:VBP	1.16	0.84	0.56	1.13
she:PRP	0.82	1.07	0.98	0.79
needs:VBZ	1.16	0.82	0.65	1.16
to:TO	1.25	1.25	1.25	1.25
gather:VB	1.16	0.83	0.57	1.11
together:RB	1.47	0.91	0.90	1.46
one:CD	1.24	1.30	1.30	1.24
accessible:JJ	1.13	0.96	0.96	1.12
place:NN	1.11	0.82	0.82	1.08
her:PRP$	0.82	1.07	0.98	0.79
scattered:JJ	1.13	0.96	0.90	1.11
disaster:NN	1.12	0.82	0.80	1.07
supplies:NNS	1.12	0.82	0.75	1.05

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.66 Alignment.score
1.00 0.52 Alignment.isGood
-1.00 0.45 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "lives" modifying "Linda_Johnson" is dropped on aligned hypothesis word "Linda_Johnson"
1.00 1.00 Factive.positiveStatement : Valid pattern: "lives" X entails X
-0.10 0.50 NullPunisher.functionWord : is
0.50 1.00 Adjunct.dropPosCxt&Align.veryGood :
Hand-tuned score (dot product of above): 3.8227
Threshold: -0.1576

Inference ID: 87

Txt: Mexico City (Mexico), 12 Jan 90 (DPA) - Salvadoran Social Democratic politician Hector Oqueli Colindres was kidnapped today, in Guatemala city, along with Guatemalan Social Democratic leader Gilda Flores, his party, in Mexico City, reported.

Hyp: Hector Oqueli Colindres comes from El Salvador. (yes)

	Hector_Oqueli_Colindres NNS	comes VBZ	El_Salvador NNP
NO_WORD	0.99	0.17	0.15
Mexico_City:NNP	0.99	1.07	0.99
Mexico:NNP	1.24	1.07	0.80
12:CD	0.98	1.27	1.24
Jan:NNP	1.24	1.07	1.00
90:CD	0.95	1.13	1.24
DPA:NNP	1.05	1.07	0.82
Salvadoran:JJ	0.97	1.22	0.39
Social:NNP	1.05	1.05	0.99
Democratic:JJ	1.09	1.22	1.03
politician:NN	1.82	0.74	1.06
Hector_Oqueli_Colindres:NNS	1.16	1.07	0.95
was:VBD	1.16	0.90	1.16
kidnapped:VBN	1.07	0.64	1.14
today:NN	0.98	0.61	1.10
Guatemala_city:NNP	1.47	1.07	0.98
along:RB	1.57	0.91	1.47
with:IN	0.99	1.24	1.57
Guatemalan:NNP	0.97	1.07	0.98
Social:NNP	1.05	1.05	0.99
Democratic:JJ	1.12	1.22	1.03
leader:NN	0.81	0.77	1.07
Gilda_Flores:NNS	0.99	1.07	0.93
his:PRP$	0.99	1.07	0.99
party:NN	1.07	0.82	1.10
Mexico_City:NNP	0.99	1.07	0.99
reported:VBD	1.16	0.71	1.16

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.62 Alignment.score
1.00 0.28 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "El_Salvador" modifying "comes"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "politician" modifying "Hector_Oqueli_Colindres" is dropped on aligned hypothesis word "Hector_Oqueli_Colindres"
-2.00 1.00 Location.mismatch : no clear info of matching: come(X, prep_from)
-3.00 1.00 NullPunisher.entity : El_Salvador
-4.00 0.22 NullPunisher.other : comes
-2.00 1.00 RootEntailment.unalignedRoot : "comes" not aligned to anything
Hand-tuned score (dot product of above): -8.2622
Threshold: -0.1576

Inference ID: 90

Txt: From Joss Stone to Keith Richards to Sting, the artists joining Les on the rock-charged album share one clear common bond: Les Paul is one of their most respected and beloved mentors.

Hyp: Eric Clapton is in business with Les Paul. (don't know)

	Eric_Clapton NNP	is VBZ	business NN	Les_Paul NNP
NO_WORD	0.78	0.17	0.07	0.13
Joss_Stone:NNP	0.78	1.07	1.07	0.79
Keith_Richards:NNP	1.07	1.07	1.14	0.81
Sting:NNP	1.25	0.82	0.80	1.07
the:DT	1.07	1.25	1.00	1.25
artists:NNS	1.16	0.82	0.89	1.12
joining:VBG	0.82	0.82	0.65	1.16
Les:NNP	1.25	1.03	1.12	0.25
the:DT	1.08	1.25	1.00	1.25
rock-charged:JJ	1.07	0.96	0.88	1.13
album:NN	1.07	0.82	0.88	1.07
share:NN	1.24	0.82	0.78	1.10
one:CD	1.13	1.30	1.33	1.24
clear:JJ	1.13	0.96	0.88	1.12
common:JJ	1.07	0.96	0.88	1.13
bond:NN	0.82	0.82	0.78	1.11
Les_Paul:NNP	1.16	1.07	1.09	1.82
is:VBZ	1.24	0.78	0.91	1.16
one:CD	1.07	1.30	1.33	1.24
their:PRP$	1.07	0.82	0.89	1.12
most:RBS	1.47	0.91	1.21	1.47
respected:JJ	1.12	0.96	0.59	1.09
beloved:JJ	1.13	0.96	0.82	1.12
mentors:NNS	1.07	0.82	0.70	1.06

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.56 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.71 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "business" modifying "is"
-3.00 1.00 NullPunisher.entity : Eric_Clapton
-4.00 0.17 NullPunisher.other : business
Hand-tuned score (dot product of above): -4.5952
Threshold: -0.1576

Inference ID: 91

Txt: Japan's Toshiba Corp. announced Tuesday that it has developed the first laptop computer with its new HD DVD drive: a next-generation disc format it is promoting over a rival standard pushed by Sony Corp.

Hyp: Toshiba Corp.'s headquarters is in Japan. (yes)

	Toshiba_Corp. NNP	headquarters NN	is VBZ	Japan NNP
NO_WORD	1.11	0.28	0.17	0.07
Japan:NNP	1.82	1.10	1.07	1.82
Toshiba_Corp.:NNP	1.16	1.13	1.07	1.11
announced:VBD	1.07	0.65	0.87	1.16
Tuesday:NNP	1.57	1.13	1.07	1.06
that:IN	1.82	1.31	1.24	1.54
it:PRP	1.82	1.13	1.07	1.11
has:VBZ	1.16	0.91	1.01	1.16
developed:VBN	1.16	0.80	0.84	1.16
the:DT	1.25	1.00	1.25	1.25
first:JJ	1.05	1.13	1.18	1.05
laptop_computer:NN	1.10	0.75	0.82	1.07
its:PRP$	1.82	1.13	1.07	1.11
new:JJ	1.13	0.76	0.96	1.13
HD:NNP	1.07	0.82	0.82	1.07
DVD:NNP	1.10	0.74	0.82	1.05
drive:NN	1.15	0.67	0.82	1.14
a:DT	1.25	1.00	1.25	1.25
next-generation:JJ	1.13	0.85	0.96	1.13
disc:NN	1.19	0.79	0.74	1.12
format:NN	1.11	0.83	0.82	1.09
it:PRP	1.82	1.13	1.07	1.11
is:VBZ	1.16	0.91	0.78	1.16
promoting:VBG	1.16	0.81	0.88	1.16
a:DT	1.25	1.00	1.25	1.25
rival:JJ	1.13	0.72	0.96	1.09
standard:NN	1.23	0.91	0.82	1.21
pushed:VBN	1.16	0.88	0.85	1.16
Sony_Corp.:NNP	0.16	1.16	1.07	1.11

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.15 Alignment.score
1.00 0.39 Alignment.isGood
-1.00 0.58 Alignment.isBad
-0.10 0.00 Alignment.numWordNotAligned
0.10 4.00 Alignment.hypSpan
0.10 0.25 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "DVD" modifying "drive" is dropped on aligned hypothesis word "headquarters"
1.00 1.00 WorldKnowledge.match : Locations match: both are talking about "Japan"
Hand-tuned score (dot product of above): 2.8832
Threshold: -0.1576

Inference ID: 93

Txt: Tilda Swinton has a prominent role as the White Witch in "The Chronicles of Narnia: The Lion, The Witch and The Wardrobe", coming out in December.

Hyp: Tilda Swinton plays the part of the White Witch. (yes)

	Tilda_Swinton NNP	plays VBZ	the DT	part NN	the DT	White_Witch NNP
Tilda_Swinton:NNP	1.82	1.07	0.96	1.07	0.96	0.93
has:VBZ	1.16	0.79	0.76	0.87	0.76	1.16
a:DT	1.25	1.25	1.31	1.00	1.31	1.25
prominent:JJ	1.13	0.88	0.76	0.83	0.76	1.13
role:NN	1.07	0.33	0.67	0.06	0.67	1.18
the:DT	1.25	1.25	0.32	1.00	0.32	1.25
White_Witch:NNP	0.76	1.07	0.96	1.25	0.96	1.65
in:IN	1.57	1.24	1.05	1.31	1.05	1.57
The:DT	1.25	1.25	0.01	1.00	0.01	1.25
Chronicles:NNS	1.07	0.82	0.71	1.00	0.71	1.16
Narnia:NNP	0.99	1.07	0.96	1.03	0.96	0.82
The:DT	1.25	1.25	0.01	1.00	0.01	1.25
Lion:NNP	0.99	1.05	0.96	1.11	0.96	0.77
The:DT	1.25	1.25	0.01	1.00	0.01	1.25
Witch:NNP	0.95	1.07	0.96	1.16	0.96	0.25
The:DT	1.25	1.25	0.01	1.00	0.01	1.25
Wardrobe:NN	0.99	1.07	0.96	1.23	0.96	0.99
coming_out:VBG	1.16	0.65	0.76	0.91	0.76	1.13
December:NNP	0.99	1.07	0.96	1.23	0.96	1.06
NO_WORD	0.28	0.17	0.82	0.09	0.82	0.04

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.07 Alignment.score
1.00 0.37 Alignment.isGood
-1.00 0.60 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "coming_out" modifying "role" is dropped on aligned hypothesis word "part"
-0.10 1.00 NullPunisher.functionWord : the
1.00 1.00 Quant.equivalent : Replacing the quantifier "a" by an equivalent quantifier "the" preserves truth.
Hand-tuned score (dot product of above): 2.5020
Threshold: -0.1576

Inference ID: 97

Txt: Patricia Amy Messier and Eugene W. Weaver were married May 28 at St. Clare Roman Catholic Church in North Palm Beach.

Hyp: Eugene W. Weaver is the husband of Patricia Amy. (yes)

	Eugene_W._Weaver NNP	is VBZ	the DT	husband NN	Patricia_Amy NNP
Patricia_Amy_Messier:NNP	0.78	1.07	0.96	1.07	0.41
Eugene_W._Weaver:NNP	1.82	1.07	0.96	1.00	0.82
were:VBD	1.16	0.10	0.73	0.91	1.16
married:VBN	1.16	0.72	0.76	0.18	1.15
May:NNP	1.00	1.07	0.96	1.10	0.99
28:CD	1.24	1.30	0.89	1.33	1.24
St._Clare_Roman_Catholic_Church:NNP	0.99	1.07	0.96	1.07	0.99
North_Palm_Beach:NNP	0.94	1.07	0.96	1.07	0.97
NO_WORD	0.28	1.43	0.82	0.12	0.04

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.99 Alignment.score
1.00 0.36 Alignment.isGood
-1.00 0.62 Alignment.isBad
-0.10 2.00 Alignment.numWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.60 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "St._Clare_Roman_Catholic_Church" modifying "married" is dropped on aligned hypothesis word "husband"
-0.10 1.00 NullPunisher.functionWord : the
-0.10 0.50 NullPunisher.functionWord : is
Hand-tuned score (dot product of above): 1.1374
Threshold: -0.1576

Inference ID: 102

Txt: FMLN reports to our people, and to the people of the world, that the massacre against the Salvadoran Workers National Union Federation [Fenastras] was carried out by Colonel Elena Fuente, as the head of the morbid death squad of the army's 1st Infantry Brigade, in response to our military attack on the army staff.

Hyp: Fenastras was attacked by FMLN. (don't know)

	Fenastras NNS	was VBD	attacked VBN	FMLN NNP
FMLN:NNP	0.82	1.07	1.07	1.82
reports:VBZ	1.11	0.85	0.62	1.16
our:PRP$	1.55	0.97	0.97	1.55
people:NNS	1.07	0.82	0.72	1.07
the:DT	1.25	1.25	1.25	1.25
people:NNS	1.07	0.82	0.72	1.07
the:DT	1.25	1.25	1.25	1.25
world:NN	1.07	0.82	0.82	1.05
that:IN	1.57	1.21	1.24	1.57
the:DT	1.25	1.25	1.25	1.25
massacre:NN	1.06	0.82	0.38	1.07
the:DT	1.25	1.25	1.25	1.25
Salvadoran_Workers_National_Union_Federation:NNP	0.82	1.07	1.07	0.82
Fenastras:NNP	1.82	1.07	1.06	0.82
was:VBD	1.16	0.78	0.82	1.16
carried_out:VBN	1.16	0.91	0.73	1.16
Colonel:NNP	1.07	0.82	0.80	1.05
Elena_Fuente:NNP	0.92	1.07	1.07	0.95
the:DT	1.25	1.25	1.25	1.25
head:NN	1.07	0.78	0.66	1.07
the:DT	1.25	1.25	1.25	1.25
morbid:VBN	1.16	0.56	0.56	1.16
death_squad:NN	1.03	0.82	0.78	1.07
the:DT	1.25	1.25	1.25	1.25
army:NN	1.07	0.82	0.43	1.07
1st:NNP	1.07	0.82	0.76	1.07
Infantry_Brigade:NNP	0.81	1.07	1.07	0.82
response:NN	1.04	0.82	0.55	1.07
to:TO	1.25	1.25	1.25	1.25
our:PRP$	1.55	0.97	0.97	1.55
military:JJ	1.12	0.96	0.60	1.13
attack:NN	1.06	0.82	1.09	1.07
the:DT	1.25	1.25	1.25	1.25
army:NN	1.07	0.82	0.43	1.07
staff:NN	1.07	0.82	0.67	1.07
NO_WORD	0.15	1.57	0.17	0.21

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.58 Alignment.score
1.00 0.50 Alignment.isGood
-1.00 0.47 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.25 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "staff" modifying "attack" is dropped on aligned hypothesis word "attacked"
-1.00 1.00 Factive.unknownPassage : Unknown: "report" X doesn't tell anything about X
-0.10 0.50 NullPunisher.functionWord : was
Hand-tuned score (dot product of above): 1.1782
Threshold: -0.1576

Inference ID: 108

Txt: Two new Les Paul books arrive in stores this year: The Les Paul Legacy by Rob Lawrence is due from Hal Leonard Publishing on October 27.

Hyp: Rob Lawrence works for The Les Paul Legacy. (don't know)

	Rob_Lawrence NNP	works VBZ	The DT	Les_Paul_Legacy NNP
Two:CD	1.24	1.30	0.82	1.24
new:JJ	1.13	0.80	0.76	1.13
Les_Paul:JJ	0.88	1.22	1.02	0.32
books:NNS	1.09	0.27	0.71	1.11
arrive:VBP	1.16	0.72	0.76	1.16
stores:NNS	1.07	0.63	0.68	1.10
this:DT	1.25	1.23	1.21	1.25
year:NN	1.14	0.82	0.71	1.10
The:DT	1.25	1.25	0.32	1.25
Les_Paul_Legacy:NNP	0.77	1.07	0.96	1.82
Rob_Lawrence:NNP	1.82	1.07	0.96	0.77
is:VBZ	1.16	0.85	0.76	1.16
due:JJ	1.13	0.85	0.69	1.13
Hal_Leonard_Publishing:VBG	1.04	0.92	1.01	1.04
October:NNP	1.03	1.07	0.96	1.00
27:CD	1.24	1.30	0.89	1.24
NO_WORD	0.28	0.17	0.82	0.05

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.22 Alignment.score
1.00 0.41 Alignment.isGood
-1.00 0.56 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.75 Alignment.txtSpan
-4.00 0.30 NullPunisher.other : works
-2.00 1.00 RootEntailment.unalignedRoot : "works" not aligned to anything
Hand-tuned score (dot product of above): -1.9569
Threshold: -0.1576

Inference ID: 123

Txt: Farabundo Marti National Liberation Front's [FMLN] urban commandos have denied reports they participated in an attack on vice president-elect Francisco Merino's home.

Hyp: Farabundo Marti National Liberation Front is accused of an attack on Merino's home. (yes)

	Farabundo_Marti_National_Liberation_Front NNP	is VBZ	accused VBN	an DT	attack NN	Merino NNP	home NN
NO_WORD	1.82	1.57	0.17	0.82	0.04	0.12	0.02
Farabundo_Marti_National_Liberation_Front:NNP	0.82	1.07	1.07	0.96	1.07	0.99	1.07
FMLN:NNP	1.13	1.07	1.07	0.96	1.07	0.99	1.07
urban:JJ	1.07	0.96	0.96	0.73	0.86	1.11	0.84
commandos:NN	1.16	0.82	0.60	0.71	0.36	1.06	0.81
have:VBP	1.16	0.98	0.78	0.76	0.91	1.16	0.80
denied:VBN	1.16	0.77	0.02	0.76	0.71	1.09	0.85
reports:VBZ	0.82	0.85	0.55	0.76	0.83	1.15	0.78
they:PRP	0.82	1.07	1.07	0.96	1.07	0.99	1.07
participated:VBD	1.16	0.75	0.21	0.76	0.71	1.16	0.91
an:DT	1.25	1.25	1.25	0.32	1.00	1.25	1.00
attack:NN	1.07	0.82	0.40	0.71	1.82	1.07	0.87
vice:NN	1.07	0.82	0.80	0.71	0.88	1.07	0.80
president-elect:NNP	1.07	0.82	0.72	0.71	0.82	1.07	0.89
Francisco_Merino:NNP	0.95	1.07	1.07	0.96	1.07	0.70	1.07
home:NN	1.07	0.82	0.80	0.71	0.87	1.07	1.82

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.39 Alignment.score
1.00 0.45 Alignment.isGood
-1.00 0.52 Alignment.isBad
-0.10 1.00 Alignment.numWordNotAligned
0.10 5.00 Alignment.hypSpan
0.10 0.29 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "president-elect" modifying "Francisco_Merino" is dropped on aligned hypothesis word "Merino"
-0.10 0.50 NullPunisher.functionWord : is
Hand-tuned score (dot product of above): 2.1990
Threshold: -0.1576

Inference ID: 132

Txt: The human rights promotion and protection secretariat of the FMLN strongly condemns the massacre of five Spanish-born Jesuit priests, and three Salvadoran citizens, carried out by the Salvadoran Armed Forces Soldiers.

Hyp: The Salvadoran Armed Forces Soldiers murdered three Salvadoran citizens. (yes)

	The DT	Salvadoran_Armed_Forces_Soldiers NNPS	murdered VBN	three CD	Salvadoran JJ	citizens NNS
The:DT	0.32	1.25	1.25	1.07	1.44	1.00
human:JJ	0.76	1.13	0.93	1.13	1.19	0.61
rights:NNS	0.71	1.07	0.74	1.29	1.24	0.39
promotion:NN	0.71	1.07	0.82	1.25	1.23	0.89
protection:NN	0.71	1.07	0.82	1.29	1.22	0.59
secretariat:NN	0.71	1.07	0.80	1.29	1.19	0.51
the:DT	0.01	1.25	1.25	1.07	1.44	1.00
FMLN:NN	0.96	0.82	1.07	1.20	1.15	1.07
strongly:RB	1.09	1.47	0.84	1.36	1.30	1.17
condemns:VBZ	0.76	1.16	0.51	1.39	1.28	0.47
the:DT	0.01	1.25	1.25	1.07	1.44	1.00
massacre:NN	0.71	1.07	0.21	1.22	1.19	0.60
five:CD	0.86	1.24	1.27	0.30	0.91	1.33
Spanish-born:NNP	0.96	0.99	1.07	1.20	0.93	1.09
Jesuit:NNP	0.96	0.99	1.07	1.20	0.99	1.07
priests:NNS	0.71	1.07	0.39	1.23	1.24	0.46
three:CD	0.78	1.24	1.16	1.25	0.91	1.31
Salvadoran:JJ	1.02	0.07	1.19	1.04	1.36	1.13
citizens:NNS	0.71	1.07	0.73	1.27	1.24	1.82
carried_out:VBN	0.76	1.16	0.71	1.39	1.24	0.89
the:DT	0.01	1.25	1.25	1.07	1.44	1.00
Salvadoran_Armed_Forces_Soldiers:NNPS	0.96	1.82	1.07	1.20	0.04	1.07
NO_WORD	0.82	0.28	0.17	0.52	0.11	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 1.56 Alignment.score
1.00 0.49 Alignment.isGood
-1.00 0.48 Alignment.isBad
-0.10 0.00 Alignment.numWordNotAligned
0.10 6.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "priests" modifying "massacre" is dropped on aligned hypothesis word "murdered"
Hand-tuned score (dot product of above): 2.7218
Threshold: -0.1576

Inference ID: 134

Txt: Jack Straw, the Foreign Secretary, will meet his Brazilian counterpart, Celso Amorim, in London today.

Hyp: Jack Straw is a partner of Celso Amorim. (don't know)

	Jack_Straw NNP	is VBZ	a DT	partner NN	Celso_Amorim NNP
NO_WORD	1.82	1.43	0.82	0.12	0.04
Jack_Straw:NNP	1.25	1.07	0.96	1.06	0.93
the:DT	1.07	1.25	1.31	1.00	1.25
Foreign:NNP	1.04