Txt/Hyp term similarities

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

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

Inference ID: 001

Txt: An Italian became the world's greatest tenor.

Hyp: There was an Italian who became the world's greatest tenor. (yes)

	There EX	was VBD	an DT	Italian JJ	who WP	became VBD	the DT	world NN	greatest JJS	tenor NN
An:DT	1.31	1.21	0.13	1.44	1.16	1.25	1.31	1.00	1.18	1.00
Italian:JJ	1.02	1.22	1.02	0.74	1.30	1.22	1.02	1.13	1.18	1.13
became:VBD	0.74	0.93	0.76	1.28	0.54	1.32	0.76	0.91	0.99	0.89
the:DT	1.20	1.25	1.31	1.44	1.09	1.25	2.42	1.00	1.18	1.00
world:NN	0.71	0.82	0.71	1.24	1.58	0.82	0.71	0.28	0.38	0.84
greatest:JJS	0.74	0.96	0.76	1.18	1.05	0.93	0.76	0.27	0.74	0.72
tenor:NN	0.66	0.82	0.71	1.24	1.58	0.80	0.71	0.84	0.83	0.28
NO_WORD	0.29	0.17	0.82	0.23	0.92	0.25	0.82	0.12	0.11	0.10

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.34 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 10.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "greatest" modifying "tenor"
0.00 1.00 NegPolarity.hypNegWord : "greatest": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : became
-1.00 1.00 NullPunisher.other : tenor
-0.10 1.00 NullPunisher.functionWord : who
-0.10 1.00 NullPunisher.functionWord : There
-1.00 1.00 NullPunisher.other : greatest
-0.10 1.00 NullPunisher.article : an
-0.10 1.00 NullPunisher.article : the
-3.00 1.00 NullPunisher.entity : Italian
-0.05 1.00 NullPunisher.aux : was
-1.00 1.00 NullPunisher.other : world
-2.00 1.00 RootEntailment.unalignedRoot : "was" not aligned to anything
Hand-tuned score (dot product of above): -11.6465
Threshold: -9.4738

Inference ID: 002

Txt: Every Italian man wants to be a great tenor. Some Italian men are great tenors.

Hyp: There are Italian men who want to be a great tenor. (yes)

	There EX	are VBP	Italian JJ	men NNS	who WP	want VBP	to TO	be VB	a DT	great JJ	tenor NN
Every:DT	1.22	1.25	1.44	1.00	1.16	1.25	1.31	1.25	1.31	1.14	0.95
Italian:JJ	1.02	1.22	0.74	1.13	1.30	1.22	1.02	1.22	1.02	1.19	1.13
man:NN	0.71	0.82	1.24	0.01	1.58	0.72	0.71	0.82	0.71	0.85	0.80
wants:VBZ	0.76	0.80	1.28	0.82	0.54	1.10	0.76	0.80	0.76	0.93	0.86
to:TO	1.31	1.25	1.44	1.00	1.12	1.25	2.42	1.25	1.31	1.18	0.96
be:VB	0.76	0.14	1.28	0.86	0.54	0.88	0.76	1.32	0.76	1.02	0.91
a:DT	1.31	1.25	1.44	1.00	1.16	1.25	1.31	1.25	2.42	1.18	1.00
great:JJ	0.72	0.90	1.19	0.88	1.05	0.93	0.76	0.96	0.76	0.74	0.60
tenor:NN	0.66	0.82	1.24	0.79	1.58	0.79	0.67	0.82	0.71	0.70	0.28
Some:DT	1.28	1.22	1.44	0.96	1.16	1.25	1.31	1.25	1.31	1.18	1.00
Italian:JJ	1.02	1.22	0.74	1.13	1.30	1.22	1.02	1.22	1.02	1.19	1.13
men:NNS	0.71	0.82	1.24	0.28	1.58	0.72	0.71	0.77	0.71	0.99	0.79
are:VBP	0.70	1.32	1.28	0.91	0.54	0.85	0.76	0.14	0.76	0.96	0.91
great:JJ	0.72	0.90	1.19	0.88	1.05	0.93	0.76	0.96	0.76	0.74	0.60
tenors:NNS	0.65	0.82	1.23	0.66	1.58	0.69	0.71	0.82	0.71	0.99	0.99
NO_WORD	0.29	0.17	0.11	0.28	0.92	0.25	1.55	0.34	0.82	0.11	0.10

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.58 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.71 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.27 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "great" modifying "tenor"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenor"
-0.05 1.00 NullPunisher.aux : are
-3.00 1.00 NullPunisher.entity : Italian
-0.10 1.00 NullPunisher.functionWord : who
-0.10 1.00 NullPunisher.article : a
-0.10 1.00 NullPunisher.functionWord : to
-0.10 1.00 NullPunisher.functionWord : There
-1.00 1.00 NullPunisher.other : great
-0.05 1.00 NullPunisher.aux : be
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -7.5273
Threshold: -9.4738

Inference ID: 003

Txt: All Italian men want to be a great tenor. Some Italian men are great tenors.

Hyp: There are Italian men who want to be a great tenor. (yes)

	There EX	are VBP	Italian JJ	men NNS	who WP	want VBP	to TO	be VB	a DT	great JJ	tenor NN
All:DT	1.31	1.18	1.44	1.00	1.16	1.22	1.31	1.25	1.31	1.18	1.00
Italian:JJ	1.02	1.22	0.74	1.13	1.30	1.22	1.02	1.22	1.02	1.19	1.13
men:NNS	0.71	0.82	1.24	0.28	1.58	0.72	0.71	0.77	0.71	0.99	0.79
want:VBP	0.76	0.77	1.28	0.81	0.51	1.32	0.76	0.80	0.76	0.99	0.88
to:TO	1.31	1.25	1.44	1.00	1.12	1.25	2.42	1.25	1.31	1.18	0.96
be:VB	0.76	0.14	1.28	0.86	0.54	0.88	0.76	1.32	0.76	1.02	0.91
a:DT	1.31	1.25	1.44	1.00	1.16	1.25	1.31	1.25	2.42	1.18	1.00
great:JJ	0.72	0.90	1.19	0.88	1.05	0.93	0.76	0.96	0.76	0.74	0.60
tenor:NN	0.66	0.82	1.24	0.79	1.58	0.79	0.67	0.82	0.71	0.70	0.28
Some:DT	1.28	1.22	1.44	0.96	1.16	1.25	1.31	1.25	1.31	1.18	1.00
Italian:JJ	1.02	1.22	0.74	1.13	1.30	1.22	1.02	1.22	1.02	1.19	1.13
men:NNS	0.71	0.82	1.24	0.28	1.58	0.72	0.71	0.77	0.71	0.99	0.79
are:VBP	0.70	1.32	1.28	0.91	0.54	0.85	0.76	0.14	0.76	0.96	0.91
great:JJ	0.72	0.90	1.19	0.88	1.05	0.93	0.76	0.96	0.76	0.74	0.60
tenors:NNS	0.65	0.82	1.23	0.66	1.58	0.69	0.71	0.82	0.71	0.99	0.99
NO_WORD	0.29	0.17	0.11	0.28	0.92	0.25	1.55	0.34	0.82	0.11	0.10

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.48 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 10.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.09 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "great" modifying "tenor"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenor"
-0.10 1.00 NullPunisher.functionWord : There
-1.00 1.00 NullPunisher.other : great
-1.00 1.00 NullPunisher.other : men
-0.10 1.00 NullPunisher.article : a
-3.00 1.00 NullPunisher.entity : Italian
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : want
-0.10 1.00 NullPunisher.functionWord : who
-0.05 1.00 NullPunisher.aux : be
-0.10 1.00 NullPunisher.functionWord : to
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -9.8908
Threshold: -9.4738

Inference ID: 004

Txt: Each Italian tenor wants to be great. Some Italian tenors are great.

Hyp: There are Italian tenors who want to be great. (yes)

	There EX	are VBP	Italian JJ	tenors RB	who WP	want VBP	to TO	be VB	great JJ
Each:DT	1.31	1.22	1.44	1.45	1.16	1.20	1.31	1.25	1.16
Italian:JJ	1.02	1.22	0.74	1.25	1.30	1.22	1.02	1.22	1.19
tenor:NN	0.66	0.82	1.24	0.48	1.58	0.79	0.67	0.82	0.70
wants:VBZ	0.76	0.80	1.28	1.05	0.54	1.10	0.76	0.80	0.93
to:TO	1.31	1.25	1.44	1.45	1.12	1.25	2.42	1.25	1.18
be:VB	0.76	0.14	1.28	1.11	0.54	0.88	0.76	1.32	1.02
great:JJ	0.72	0.90	1.19	1.01	1.05	0.93	0.76	0.96	0.74
Some:DT	1.28	1.22	1.44	1.45	1.16	1.25	1.31	1.25	1.18
Italian:JJ	1.02	1.22	0.74	1.25	1.30	1.22	1.02	1.22	1.19
tenors:NNS	0.65	0.82	1.23	2.58	1.58	0.69	0.71	0.82	0.99
are:VBP	0.70	1.32	1.28	1.11	0.54	0.85	0.76	0.14	0.96
great:JJ	0.72	0.90	1.19	1.01	1.05	0.93	0.76	0.96	0.74
NO_WORD	0.29	0.17	0.09	0.04	1.07	0.31	1.55	1.43	0.32

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.91 Alignment.score
1.00 0.34 Alignment.isGood
-1.00 0.64 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.22 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Italian" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-0.10 1.00 NullPunisher.functionWord : who
-0.10 1.00 NullPunisher.functionWord : There
-0.05 1.00 NullPunisher.aux : be
-1.00 1.00 NullPunisher.other : great
-0.05 1.00 NullPunisher.aux : are
-3.00 1.00 NullPunisher.entity : Italian
-0.10 1.00 NullPunisher.functionWord : to
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -5.8723
Threshold: -9.4738

Inference ID: 005

Txt: The really ambitious tenors are Italian.

Hyp: There are really ambitious tenors who are Italian. (yes)

	There EX	are VBP	really RB	ambitious JJ	tenors RB	who WP	are VBP	Italian JJ
The:DT	1.20	1.18	1.45	1.18	1.45	1.09	1.18	1.44
really:RB	1.09	0.91	1.23	1.05	0.70	1.05	0.91	1.25
ambitious:JJ	0.76	0.96	1.01	0.74	0.82	1.05	0.96	1.19
tenors:NNS	0.65	0.82	1.15	0.79	2.58	1.58	0.82	1.23
are:VBP	0.70	1.32	1.11	1.02	1.11	0.54	1.32	1.28
Italian:JJ	1.02	1.22	1.22	1.19	1.25	1.30	1.22	0.74
NO_WORD	0.29	0.17	0.04	0.09	0.04	1.07	0.31	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.53 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.12 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "really" modifying "ambitious"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "ambitious" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-1.00 1.00 NullPunisher.other : ambitious
-1.00 1.00 NullPunisher.other : really
-0.05 1.00 NullPunisher.aux : are
-0.05 1.00 NullPunisher.aux : are
-0.10 1.00 NullPunisher.functionWord : There
-3.00 1.00 NullPunisher.entity : Italian
-0.10 1.00 NullPunisher.functionWord : who
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -8.3104
Threshold: -9.4738

Inference ID: 006

Txt: No really great tenors are modest.

Hyp: There are really great tenors who are modest. (don't know)

	There EX	are VBP	really RB	great JJ	tenors RB	who WP	are VBP	modest JJ
No:DT	1.31	1.25	1.45	1.18	1.45	1.12	1.25	1.18
really:RB	1.09	0.91	1.23	0.58	0.70	1.05	0.91	1.05
great:JJ	0.72	0.90	0.54	0.74	1.01	1.05	0.90	0.70
tenors:NNS	0.65	0.82	1.15	0.99	2.58	1.58	0.82	0.89
are:VBP	0.70	1.32	1.11	0.96	1.11	0.54	1.32	1.02
modest:JJ	0.74	0.96	1.01	0.70	0.92	1.05	0.96	0.74
NO_WORD	0.29	0.17	0.04	0.09	0.04	1.07	0.31	0.09

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.53 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.12 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "really" modifying "great"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-0.05 1.00 NullPunisher.aux : are
-0.10 1.00 NullPunisher.functionWord : There
-0.10 1.00 NullPunisher.functionWord : who
-1.00 1.00 NullPunisher.other : really
-1.00 1.00 NullPunisher.other : modest
-1.00 1.00 NullPunisher.other : great
-0.05 1.00 NullPunisher.aux : are
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -6.3104
Threshold: -9.4738

Inference ID: 007

Txt: Some great tenors are Swedish.

Hyp: There are great tenors who are Swedish. (yes)

	There EX	are VBP	great JJ	tenors JJ	who WP	are VBP	Swedish NNP
Some:DT	1.28	1.22	1.18	1.18	1.16	1.22	1.25
great:JJ	0.72	0.90	0.74	0.93	1.05	0.90	1.13
tenors:NNS	0.65	0.82	0.99	1.93	1.58	0.82	0.97
are:VBP	0.70	1.32	0.96	1.02	0.54	1.32	1.16
Swedish:NNP	0.96	1.07	1.24	1.23	1.84	1.07	0.28
NO_WORD	0.29	0.17	0.09	0.29	0.54	0.36	0.10

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.47 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-0.05 1.00 NullPunisher.aux : are
-0.05 1.00 NullPunisher.aux : are
-3.00 1.00 NullPunisher.entity : Swedish
-0.10 1.00 NullPunisher.functionWord : who
-0.10 1.00 NullPunisher.functionWord : There
-1.00 1.00 NullPunisher.other : great
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -6.2944
Threshold: -9.4738

Inference ID: 008

Txt: Many great tenors are German.

Hyp: There are great tenors who are German. (yes)

	There EX	are VBP	great JJ	tenors RB	who WP	are VBP	German JJ
Many:JJ	0.76	0.93	0.93	1.01	1.05	0.93	1.15
great:JJ	0.72	0.90	0.74	1.01	1.05	0.90	1.09
tenors:NNS	0.65	0.82	0.99	2.58	1.58	0.82	1.21
are:VBP	0.70	1.32	0.96	1.11	0.54	1.32	1.28
German:JJ	1.00	1.22	1.09	1.23	1.30	1.22	0.74
NO_WORD	0.29	0.17	0.09	0.04	1.07	0.31	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.61 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-3.00 1.00 NullPunisher.entity : German
-0.05 1.00 NullPunisher.aux : are
-0.10 1.00 NullPunisher.functionWord : There
-0.10 1.00 NullPunisher.functionWord : who
-1.00 1.00 NullPunisher.other : great
-0.05 1.00 NullPunisher.aux : are
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -6.1099
Threshold: -9.4738

Inference ID: 009

Txt: Several great tenors are British.

Hyp: There are great tenors who are British. (yes)

	There EX	are VBP	great JJ	tenors RB	who WP	are VBP	British JJ
Several:JJ	0.72	0.96	0.90	0.96	1.05	0.96	1.19
great:JJ	0.72	0.90	0.74	1.01	1.05	0.90	1.19
tenors:NNS	0.65	0.82	0.99	2.58	1.58	0.82	1.23
are:VBP	0.70	1.32	0.96	1.11	0.54	1.32	1.28
British:JJ	1.02	1.22	1.19	1.25	1.30	1.22	0.74
NO_WORD	0.29	0.17	0.09	0.04	1.07	0.31	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.61 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-0.10 1.00 NullPunisher.functionWord : There
-0.05 1.00 NullPunisher.aux : are
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : great
-0.10 1.00 NullPunisher.functionWord : who
-3.00 1.00 NullPunisher.entity : British
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -6.1099
Threshold: -9.4738

Inference ID: 010

Txt: Most great tenors are Italian.

Hyp: There are great tenors who are Italian. (yes)

	There EX	are VBP	great JJ	tenors RB	who WP	are VBP	Italian JJ
Most:JJS	0.76	0.96	0.91	1.01	1.05	0.96	1.19
great:JJ	0.72	0.90	0.74	1.01	1.05	0.90	1.19
tenors:NNS	0.65	0.82	0.99	2.58	1.58	0.82	1.23
are:VBP	0.70	1.32	0.96	1.11	0.54	1.32	1.28
Italian:JJ	1.02	1.22	1.19	1.25	1.30	1.22	0.74
NO_WORD	0.29	0.17	0.09	0.04	1.07	0.31	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.61 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-3.00 1.00 NullPunisher.entity : Italian
-0.10 1.00 NullPunisher.functionWord : who
-0.10 1.00 NullPunisher.functionWord : There
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : great
-0.05 1.00 NullPunisher.aux : are
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -6.1099
Threshold: -9.4738

Inference ID: 011

Txt: A few great tenors sing popular music. Some great tenors like popular music.

Hyp: There are great tenors who sing popular music. (yes)

	There EX	are VBP	great JJ	tenors RB	who WP	sing VBP	popular_music NN
A:DT	1.31	1.25	1.18	1.45	1.16	1.25	1.00
few:JJ	0.76	0.96	0.93	1.01	1.05	0.96	0.88
great:JJ	0.72	0.90	0.74	1.01	1.05	0.87	0.62
tenors:NNS	0.65	0.82	0.99	2.58	1.58	0.47	0.55
sing:VBP	0.76	0.72	0.93	0.77	0.54	1.32	0.44
popular_music:NN	0.71	0.82	0.73	1.11	1.58	0.35	0.28
Some:DT	1.28	1.22	1.18	1.45	1.16	1.20	1.00
great:JJ	0.72	0.90	0.74	1.01	1.05	0.87	0.62
tenors:NNS	0.65	0.82	0.99	2.58	1.58	0.47	0.55
like:VBP	0.73	0.76	0.61	1.11	0.54	0.13	0.66
popular_music:NN	0.71	0.82	0.73	1.11	1.58	0.35	0.28
NO_WORD	0.29	0.17	0.09	0.04	1.07	0.31	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.63 Alignment.score
1.00 0.28 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "great" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-1.00 1.00 NullPunisher.other : great
-0.10 1.00 NullPunisher.functionWord : who
-1.00 1.00 NullPunisher.other : popular_music
-0.05 1.00 NullPunisher.aux : are
-0.10 1.00 NullPunisher.functionWord : There
-1.00 1.00 NullPunisher.other : sing
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -5.0239
Threshold: -9.4738

Inference ID: 013

Txt: Both leading tenors are excellent. Leading tenors who are excellent are indispensable.

Hyp: Both leading tenors are indispensable. (yes)

	Both DT	leading JJ	tenors NNS	are VBP	indispensable JJ
Both:DT	2.42	1.18	1.00	1.25	1.18
leading:JJ	0.76	0.74	0.86	0.96	0.75
tenors:NNS	0.71	0.97	0.28	0.82	0.83
are:VBP	0.76	1.02	0.91	1.32	1.02
excellent:JJ	0.76	0.91	0.88	0.96	0.66
Leading:VBG	0.76	0.84	0.89	0.81	1.02
tenors:NNS	0.71	0.97	0.28	0.82	0.83
who:WP	1.11	1.39	1.30	0.97	1.39
are:VBP	0.76	1.02	0.91	1.32	1.02
excellent:JJ	0.76	0.91	0.88	0.96	0.66
are:VBP	0.76	1.02	0.91	1.32	1.02
indispensable:JJ	0.76	0.75	0.72	0.96	0.74
NO_WORD	0.82	0.11	0.28	1.43	0.16

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.59 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.20 Alignment.txtSpan
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : Both
-1.00 1.00 NullPunisher.other : indispensable
-1.00 1.00 NullPunisher.other : tenors
-2.00 1.00 RootEntailment.unalignedRoot : "indispensable" not aligned to anything
-1.00 1.00 Structure.relMismatch : hypothesis noun modifier "leading" and aligned text noun modifier "tenors" bear the same relation ("amod") but different parents ("tenors" vs. "tenors")
Hand-tuned score (dot product of above): -6.1702
Threshold: -9.4738

Inference ID: 014

Txt: Neither leading tenor comes cheap. One of the leading tenors is Pavarotti.

Hyp: Pavarotti is a leading tenor who comes cheap. (don't know)

	Pavarotti NNP	is VBZ	a DT	leading VBG	tenor NN	who WP	comes VBZ	cheap JJ
Neither:DT	1.25	1.25	1.31	1.22	0.96	1.16	1.25	1.18
leading:JJ	1.13	0.96	0.76	1.16	0.88	1.05	0.95	0.93
tenor:NN	1.07	0.82	0.71	0.82	0.28	1.58	0.82	0.99
comes:VBZ	1.16	0.81	0.76	0.67	0.91	0.54	1.32	0.81
cheap:JJ	1.13	0.96	0.76	0.96	0.88	1.05	0.75	0.74
One:CD	1.24	1.30	0.89	1.30	1.33	1.32	1.25	1.00
the:DT	1.25	1.25	1.31	1.25	1.00	1.09	1.25	1.13
leading:JJ	1.13	0.96	0.76	1.16	0.88	1.05	0.95	0.93
tenors:NNS	1.07	0.82	0.71	0.80	0.99	1.58	0.80	0.99
is:VBZ	1.16	1.32	0.76	0.89	0.91	0.54	0.90	1.02
Pavarotti:NNP	0.28	1.07	0.96	1.07	0.99	1.84	1.07	1.24
NO_WORD	0.28	1.43	0.82	0.12	0.28	0.92	0.25	0.09

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.75 Alignment.score
1.00 0.30 Alignment.isGood
-1.00 0.67 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.25 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "comes" modifying "tenor"
-1.00 1.00 NullPunisher.other : cheap
-0.10 1.00 NullPunisher.article : a
-3.00 1.00 NullPunisher.entity : Pavarotti
-1.00 1.00 NullPunisher.other : comes
-0.10 1.00 NullPunisher.functionWord : who
-0.05 1.00 NullPunisher.aux : is
1.00 1.00 Quant.equivalent : Replacing the quantifier "the" by an equivalent quantifier "a" preserves truth.
Hand-tuned score (dot product of above): -5.2429
Threshold: -9.4738

Inference ID: 015

Txt: At least three tenors will take part in the concert.

Hyp: There are tenors who will take part in the concert. (yes)

	There EX	are VBP	tenors JJ	who WP	will MD	take_part VB	the DT	concert NN
At:IN	1.05	1.20	0.77	1.57	1.05	1.24	1.05	1.31
least:JJS	0.76	0.96	0.91	1.05	0.76	0.93	0.76	0.88
three:CD	0.76	1.25	0.83	1.32	0.89	1.16	0.78	1.33
tenors:NNS	0.65	0.82	1.93	1.58	0.71	0.77	0.71	0.55
will:MD	1.31	1.60	1.18	1.13	2.42	1.37	1.31	1.00
take_part:VB	0.73	0.75	0.97	0.54	0.87	1.32	0.76	0.88
the:DT	1.20	1.18	1.18	1.09	1.31	1.25	2.42	1.00
concert:NN	0.67	0.82	0.73	1.58	0.71	0.79	0.71	0.28
NO_WORD	0.29	0.17	0.09	0.54	1.55	0.36	0.82	0.07

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.70 Alignment.score
1.00 0.29 Alignment.isGood
-1.00 0.68 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.12 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "concert" modifying "take_part"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "three" modifying "tenors" is dropped on aligned hypothesis word "tenors"
-1.00 1.00 NullPunisher.other : take_part
-0.10 1.00 NullPunisher.functionWord : There
-0.10 1.00 NullPunisher.article : the
-0.10 1.00 NullPunisher.functionWord : who
-0.05 1.00 NullPunisher.aux : will
-1.00 1.00 NullPunisher.other : concert
-0.05 1.00 NullPunisher.aux : are
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -5.1777
Threshold: -9.4738

Inference ID: 017

Txt: An Irishman won the Nobel prize for literature.

Hyp: An Irishman won a Nobel prize. (yes)

	An DT	Irishman NNP	won VBD	a DT	Nobel_prize NN
An:DT	2.42	1.25	1.21	1.14	1.00
Irishman:NNP	0.96	0.28	1.07	0.96	1.07
won:VBD	0.71	1.16	1.32	0.76	0.57
the:DT	1.31	1.25	1.25	1.31	1.00
Nobel_prize:NN	0.71	1.07	0.49	0.71	0.28
literature:NN	0.71	1.07	0.82	0.71	0.38
NO_WORD	0.82	0.28	0.17	0.82	0.25

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.36 Alignment.score
1.00 0.23 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : Nobel_prize
-0.10 1.00 NullPunisher.article : An
-1.00 1.00 NullPunisher.other : won
-0.10 1.00 NullPunisher.article : a
-3.00 1.00 NullPunisher.entity : Irishman
-2.00 1.00 RootEntailment.unalignedRoot : "won" not aligned to anything
Hand-tuned score (dot product of above): -7.8694
Threshold: -9.4738

Inference ID: 018

Txt: Every European has the right to live in Europe. Every European is a person. Every person who has the right to live in Europe can travel freely within Europe.

Hyp: Every European can travel freely within Europe. (yes)

	Every DT	European NNP	can MD	travel VB	freely RB	Europe NNP
Every:DT	2.42	1.24	1.31	1.23	1.39	1.23
European:NNP	0.95	0.28	0.96	1.04	1.53	0.28
has:VBZ	0.76	1.16	0.93	0.94	1.11	1.16
the:DT	1.31	1.25	1.31	1.23	1.45	1.25
right:NN	0.71	1.18	0.71	0.80	1.19	1.17
to:TO	1.31	1.25	1.31	1.25	1.45	1.25
live:VB	0.73	1.16	0.87	0.59	0.96	1.16
Europe:NNP	0.94	0.28	0.96	1.04	1.56	0.28
Every:DT	2.42	1.24	1.31	1.23	1.39	1.23
European:NNP	0.95	0.28	0.96	1.04	1.53	0.28
is:VBZ	0.76	1.16	1.06	1.00	1.11	1.16
a:DT	1.31	1.25	1.31	1.25	1.45	1.25
person:NN	0.69	1.11	0.71	0.59	1.22	1.17
Every:DT	2.42	1.24	1.31	1.23	1.39	1.23
person:NN	0.69	1.11	0.71	0.59	1.22	1.17
who:WP	1.11	1.55	1.18	0.97	1.05	1.55
has:VBZ	0.76	1.16	0.93	0.94	1.11	1.16
the:DT	1.31	1.25	1.31	1.23	1.45	1.25
right:NN	0.71	1.18	0.71	0.80	1.19	1.17
to:TO	1.31	1.25	1.31	1.25	1.45	1.25
live:VB	0.73	1.16	0.87	0.59	0.96	1.16
Europe:NNP	0.94	0.28	0.96	1.04	1.56	0.28
can:MD	1.31	1.25	2.42	1.51	1.45	1.25
travel:VB	0.74	1.13	1.02	1.32	0.90	1.13
freely:RB	1.03	1.44	1.09	0.70	1.23	1.47
Europe:NNP	0.94	0.28	0.96	1.04	1.56	0.28
NO_WORD	0.82	0.28	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.53 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.17 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-1.00 1.00 NullPunisher.other : travel
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : Every
-1.00 1.00 NullPunisher.other : freely
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
Hand-tuned score (dot product of above): -4.2688
Threshold: -9.4738

Inference ID: 019

Txt: All Europeans have the right to live in Europe. Every European is a person. Every person who has the right to live in Europe can travel freely within Europe.

Hyp: All Europeans can travel freely within Europe. (yes)

	All DT	Europeans NNPS	can MD	travel VB	freely RB	Europe NNP
All:DT	2.42	1.25	1.31	1.23	1.45	1.25
Europeans:NNPS	0.96	0.28	0.96	1.06	1.55	0.46
have:VBP	0.73	1.16	0.97	0.27	1.11	1.16
the:DT	1.31	1.25	1.31	1.23	1.45	1.25
right:NN	0.71	1.18	0.71	0.80	1.19	1.17
to:TO	1.31	1.25	1.31	1.25	1.45	1.25
live:VB	0.76	1.16	0.87	0.59	0.96	1.16
Europe:NNP	0.96	0.46	0.96	1.04	1.56	0.28
Every:DT	1.31	1.25	1.31	1.23	1.39	1.23
European:NNP	0.96	1.37	0.96	1.04	1.53	0.28
is:VBZ	0.76	1.16	1.06	1.00	1.11	1.16
a:DT	1.31	1.25	1.31	1.25	1.45	1.25
person:NN	0.71	1.12	0.71	0.59	1.22	1.17
Every:DT	1.31	1.25	1.31	1.23	1.39	1.23
person:NN	0.71	1.12	0.71	0.59	1.22	1.17
who:WP	1.11	1.55	1.18	0.97	1.05	1.55
has:VBZ	0.76	1.16	0.93	0.94	1.11	1.16
the:DT	1.31	1.25	1.31	1.23	1.45	1.25
right:NN	0.71	1.18	0.71	0.80	1.19	1.17
to:TO	1.31	1.25	1.31	1.25	1.45	1.25
live:VB	0.76	1.16	0.87	0.59	0.96	1.16
Europe:NNP	0.96	0.46	0.96	1.04	1.56	0.28
can:MD	1.31	1.25	2.42	1.51	1.45	1.25
travel:VB	0.73	1.15	1.02	1.32	0.90	1.13
freely:RB	1.09	1.46	1.09	0.70	1.23	1.47
Europe:NNP	0.96	0.46	0.96	1.04	1.56	0.28
NO_WORD	0.82	0.28	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.69 Alignment.score
1.00 0.29 Alignment.isGood
-1.00 0.68 Alignment.isBad
-0.10 3.00 Alignment.nbWordNotAligned
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 "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-1.00 1.00 NullPunisher.other : freely
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : All
1.00 1.00 Quant.equivalent : Replacing the quantifier "every" by an equivalent quantifier "all" preserves truth.
-3.00 1.00 Structure.argsMismatch : args have different parents but same relations: text "European" <-nsubj-- "person" vs. hyp "Europeans" <-nsubj-- "travel", which aligned to text "have"
Hand-tuned score (dot product of above): -2.9090
Threshold: -9.4738

Inference ID: 020

Txt: Each European has the right to live in Europe. Every European is a person. Every person who has the right to live in Europe can travel freely within Europe.

Hyp: Each European can travel freely within Europe. (yes)

	Each DT	European NNP	can MD	travel VB	freely RB	Europe NNP
Each:DT	2.42	1.00	1.53	1.51	1.71	1.17
European:NNP	0.71	0.28	0.96	1.04	1.53	0.28
has:VBZ	0.98	1.16	0.93	0.94	1.11	1.16
the:DT	1.56	1.25	1.31	1.23	1.45	1.25
right:NN	0.94	1.18	0.71	0.80	1.19	1.17
to:TO	1.56	1.25	1.31	1.25	1.45	1.25
live:VB	1.01	1.16	0.87	0.59	0.96	1.16
Europe:NNP	0.87	0.28	0.96	1.04	1.56	0.28
Every:DT	1.54	1.24	1.31	1.23	1.39	1.23
European:NNP	0.71	0.28	0.96	1.04	1.53	0.28
is:VBZ	1.01	1.16	1.06	1.00	1.11	1.16
a:DT	1.56	1.25	1.31	1.25	1.45	1.25
person:NN	0.96	1.11	0.71	0.59	1.22	1.17
Every:DT	1.54	1.24	1.31	1.23	1.39	1.23
person:NN	0.96	1.11	0.71	0.59	1.22	1.17
who:WP	1.37	1.55	1.18	0.97	1.05	1.55
has:VBZ	0.98	1.16	0.93	0.94	1.11	1.16
the:DT	1.56	1.25	1.31	1.23	1.45	1.25
right:NN	0.94	1.18	0.71	0.80	1.19	1.17
to:TO	1.56	1.25	1.31	1.25	1.45	1.25
live:VB	1.01	1.16	0.87	0.59	0.96	1.16
Europe:NNP	0.87	0.28	0.96	1.04	1.56	0.28
can:MD	1.53	1.25	2.42	1.51	1.45	1.25
travel:VB	1.01	1.13	1.02	1.32	0.90	1.13
freely:RB	1.35	1.44	1.09	0.70	1.23	1.47
Europe:NNP	0.87	0.28	0.96	1.04	1.56	0.28
NO_WORD	0.82	0.28	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.53 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.17 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-1.00 1.00 NullPunisher.other : travel
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : freely
-3.00 1.00 NullPunisher.entity : Each
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
Hand-tuned score (dot product of above): -6.2688
Threshold: -9.4738

Inference ID: 021

Txt: The residents of member states have the right to live in Europe. All residents of member states are individuals. Every individual who has the right to live in Europe can travel freely within Europe.

Hyp: The residents of member states can travel freely within Europe. (yes)

	The DT	residents NNS	member NN	states NNS	can MD	travel VB	freely RB	Europe NNP
The:DT	2.42	1.00	1.00	0.97	1.31	1.23	1.45	1.25
residents:NNS	0.71	0.28	0.79	0.87	0.71	0.69	1.27	1.09
member:NN	0.71	0.79	0.28	0.80	0.71	0.75	1.27	1.13
states:NNS	0.68	0.87	0.80	0.28	0.71	0.60	1.20	1.15
have:VBP	0.73	0.91	0.91	0.86	0.97	0.27	1.11	1.16
the:DT	0.01	1.00	1.00	0.97	1.31	1.23	1.45	1.25
right:NN	0.71	0.87	0.93	0.99	0.71	0.80	1.19	1.17
to:TO	1.26	1.00	1.00	1.00	1.31	1.25	1.45	1.25
live:VB	0.73	0.58	0.91	0.80	0.87	0.59	0.96	1.16
Europe:NNP	0.96	1.09	1.13	1.15	0.96	1.04	1.56	0.28
All:DT	1.31	1.00	1.00	1.00	1.31	1.23	1.45	1.25
residents:NNS	0.71	0.28	0.79	0.87	0.71	0.69	1.27	1.09
member:NN	0.71	0.79	0.28	0.80	0.71	0.75	1.27	1.13
states:NNS	0.68	0.87	0.80	0.28	0.71	0.60	1.20	1.15
are:VBP	0.68	0.91	0.91	0.88	1.06	0.98	1.09	1.14
individuals:NNS	0.71	0.74	0.85	0.94	0.71	0.66	0.97	1.21
Every:DT	1.31	1.00	0.94	1.00	1.31	1.23	1.39	1.23
individual:NN	0.71	0.86	0.88	0.99	0.71	0.82	1.07	1.21
who:WP	1.04	1.30	1.30	1.30	1.18	0.97	1.05	1.55
has:VBZ	0.76	0.91	0.91	0.88	0.93	0.94	1.11	1.16
the:DT	0.01	1.00	1.00	0.97	1.31	1.23	1.45	1.25
right:NN	0.71	0.87	0.93	0.99	0.71	0.80	1.19	1.17
to:TO	1.26	1.00	1.00	1.00	1.31	1.25	1.45	1.25
live:VB	0.73	0.58	0.91	0.80	0.87	0.59	0.96	1.16
Europe:NNP	0.96	1.09	1.13	1.15	0.96	1.04	1.56	0.28
can:MD	1.31	1.00	1.00	1.00	2.42	1.51	1.45	1.25
travel:VB	0.73	0.78	0.84	0.69	1.02	1.32	0.90	1.13
freely:RB	1.09	1.18	1.18	1.11	1.09	0.70	1.23	1.47
Europe:NNP	0.96	1.09	1.13	1.15	0.96	1.04	1.56	0.28
NO_WORD	0.82	0.28	0.05	0.04	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.49 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 3.00 Alignment.nbWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-0.10 1.00 NullPunisher.article : The
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : freely
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
Hand-tuned score (dot product of above): 1.9116
Threshold: -9.4738

Inference ID: 022

Txt: No delegate finished the report on time.

Hyp: No delegate finished the report. (don't know)

	No DT	delegate NN	finished VBD	the DT	report NN
No:DT	2.42	1.00	1.25	1.31	1.00
delegate:NN	0.71	0.28	0.74	0.71	0.74
finished:VBD	0.76	0.83	1.32	0.76	0.86
the:DT	1.31	1.00	1.25	2.42	1.00
report:NN	0.71	0.74	0.77	0.71	0.28
time:NN	0.71	0.86	0.68	0.61	0.91
NO_WORD	0.82	0.28	0.17	0.82	0.09

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.32 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : finished
-1.00 1.00 NullPunisher.other : report
-1.00 1.00 NullPunisher.other : No
-1.00 1.00 NullPunisher.other : delegate
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -6.8134
Threshold: -9.4738

Inference ID: 023

Txt: Some delegates finished the survey on time.

Hyp: Some delegates finished the survey. (yes)

	Some DT	delegates NNS	finished VBD	the DT	survey NN
Some:DT	2.42	1.00	1.25	1.28	0.95
delegates:NNS	0.71	0.28	0.78	0.71	0.53
finished:VBD	0.76	0.87	1.32	0.76	0.91
the:DT	1.28	1.00	1.25	2.42	1.00
survey:NN	0.66	0.53	0.82	0.71	0.28
time:NN	0.60	0.86	0.68	0.61	0.84
NO_WORD	0.82	0.28	0.17	0.82	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.32 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : survey
-1.00 1.00 NullPunisher.other : Some
-1.00 1.00 NullPunisher.other : delegates
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : finished
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -6.8134
Threshold: -9.4738

Inference ID: 024

Txt: Many delegates obtained interesting results from the survey.

Hyp: Many delegates obtained results from the survey. (yes)

	Many JJ	delegates NNS	obtained VBD	results NNS	the DT	survey NN
Many:JJ	0.74	0.88	0.96	0.88	0.76	0.88
delegates:NNS	0.99	0.28	0.80	0.63	0.71	0.53
obtained:VBD	1.02	0.89	1.32	0.85	0.76	0.61
interesting:JJ	0.93	0.77	0.90	0.71	0.76	0.74
results:NNS	0.99	0.63	0.76	0.28	0.71	0.63
the:DT	1.18	1.00	1.25	1.00	2.42	1.00
survey:NN	0.99	0.53	0.52	0.63	0.71	0.28
NO_WORD	0.11	0.28	0.17	0.09	0.82	0.15

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.13 Alignment.score
1.00 0.19 Alignment.isGood
-1.00 0.79 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "survey" modifying "obtained"
-1.00 1.00 NullPunisher.other : results
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : obtained
-1.00 1.00 NullPunisher.other : Many
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : survey
-2.00 1.00 RootEntailment.unalignedRoot : "obtained" not aligned to anything
Hand-tuned score (dot product of above): -9.1748
Threshold: -9.4738

Inference ID: 025

Txt: Several delegates got the results published in major national newspapers.

Hyp: Several delegates got the results published. (yes)

	Several JJ	delegates NNS	got VBD	the DT	results NNS	published VBD
Several:JJ	0.74	0.82	0.96	0.76	0.84	0.96
delegates:NNS	0.93	0.28	0.82	0.71	0.63	0.71
got:VBD	1.02	0.91	1.32	0.76	0.91	0.74
the:DT	1.18	1.00	1.25	2.42	1.00	1.25
results:NNS	0.95	0.63	0.82	0.71	0.28	0.64
published:VBN	1.02	0.80	0.74	0.76	0.73	2.02
major:JJ	0.93	0.88	0.96	0.76	0.88	0.95
national:JJ	0.89	0.75	0.96	0.76	0.88	0.83
newspapers:NNS	0.97	0.56	0.77	0.71	0.82	0.56
NO_WORD	0.11	0.28	0.17	0.82	0.28	0.36

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.49 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Several" modifying "delegates"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "newspapers" modifying "published" is dropped on aligned hypothesis word "published"
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : Several
-1.00 1.00 NullPunisher.other : got
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "got" not aligned to anything
Hand-tuned score (dot product of above): -5.7572
Threshold: -9.4738

Inference ID: 026

Txt: Most Europeans are resident in Europe. All Europeans are people. All people who are resident in Europe can travel freely within Europe.

Hyp: Most Europeans can travel freely within Europe. (yes)

	Most JJS	Europeans NNPS	can MD	travel VB	freely RB	Europe NNP
Most:JJS	0.74	1.13	0.76	0.96	1.01	1.13
Europeans:NNPS	1.24	0.28	0.96	1.06	1.55	0.46
are:VBP	1.02	1.16	1.06	0.98	1.09	1.14
resident:VBN	1.02	1.12	0.76	0.23	1.08	1.16
Europe:NNP	1.24	0.46	0.96	1.04	1.56	0.28
All:DT	1.18	1.25	1.31	1.23	1.45	1.25
Europeans:NNPS	1.24	0.28	0.96	1.06	1.55	0.46
are:VBP	1.02	1.16	1.06	0.98	1.09	1.14
people:NNS	0.99	1.21	0.71	0.66	1.22	1.14
All:DT	1.18	1.25	1.31	1.23	1.45	1.25
people:NNS	0.99	1.21	0.71	0.66	1.22	1.14
who:WP	1.39	1.55	1.18	0.97	1.05	1.55
are:VBP	1.02	1.16	1.06	0.98	1.09	1.14
resident:NN	0.99	0.89	0.71	0.49	1.27	1.09
Europe:NNP	1.24	0.46	0.96	1.04	1.56	0.28
can:MD	1.18	1.25	2.42	1.51	1.45	1.25
travel:VB	1.02	1.15	1.02	1.32	0.90	1.13
freely:RB	1.05	1.46	1.09	0.70	1.23	1.47
Europe:NNP	1.24	0.46	0.96	1.04	1.56	0.28
NO_WORD	0.11	0.28	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.47 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.17 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Most" modifying "Europeans" is dropped on aligned hypothesis word "Europe"
2.00 1.00 Modal.yes : actual -> possible
0.00 1.00 NegPolarity.hypNegWord : "Most": tag "JJS" is in NegPolarityMarkers list
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : freely
-1.00 1.00 NullPunisher.other : travel
-1.00 1.00 NullPunisher.other : Most
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
Hand-tuned score (dot product of above): -3.8498
Threshold: -9.4738

Inference ID: 027

Txt: A few committee members are from Sweden. All committee members are people. All people who are from Sweden are from Scandinavia.

Hyp: At least a few committee members are from Scandinavia. (yes)

	At IN	least JJS	a DT	few JJ	committee_members NNS	are VBP	Scandinavia NNP
A:DT	1.00	1.18	0.13	1.18	1.00	1.25	1.25
few:JJ	1.05	0.93	0.76	0.74	0.88	0.96	1.13
committee_members:NNS	0.50	0.99	0.71	0.99	0.28	0.82	1.06
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
Sweden:NNP	0.75	1.22	0.96	1.24	1.07	1.07	0.76
All:DT	1.00	1.18	1.31	1.18	1.00	1.18	1.25
committee_members:NNS	0.50	0.99	0.71	0.99	0.28	0.82	1.06
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
people:NNS	0.50	0.97	0.71	0.99	0.84	0.82	1.13
All:DT	1.00	1.18	1.31	1.18	1.00	1.18	1.25
people:NNS	0.50	0.97	0.71	0.99	0.84	0.82	1.13
who:WP	1.05	1.39	1.11	1.39	1.30	0.97	1.55
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
Sweden:NNP	0.75	1.22	0.96	1.24	1.07	1.07	0.76
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
Scandinavia:NNP	0.75	1.24	0.96	1.24	1.06	1.07	0.28
NO_WORD	0.73	0.29	0.82	0.11	0.28	0.17	0.15

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.24 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Scandinavia" modifying "are"
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
0.00 1.00 NegPolarity.hypNegWord : "few": is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : least
-0.05 1.00 NullPunisher.aux : are
-3.00 1.00 NullPunisher.entity : Scandinavia
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : committee_members
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : few
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -11.1707
Threshold: -9.4738

Inference ID: 028

Txt: Few committee members are from Portugal. All committee members are people. All people who are from Portugal are from southern Europe.

Hyp: There are few committee members from southern Europe. (don't know)

	There EX	are VBP	few JJ	committee_members NNS	southern JJ	Europe NNP
Few:JJ	0.76	0.96	0.76	0.88	0.93	1.13
committee_members:NNS	0.71	0.82	0.99	0.28	0.99	1.11
are:VBP	0.70	1.32	1.02	0.91	1.02	1.14
Portugal:NNP	0.96	1.07	1.24	1.07	1.19	0.80
All:DT	1.31	1.18	1.18	1.00	1.18	1.25
committee_members:NNS	0.71	0.82	0.99	0.28	0.99	1.11
are:VBP	0.70	1.32	1.02	0.91	1.02	1.14
people:NNS	0.69	0.82	0.99	0.84	0.99	1.14
All:DT	1.31	1.18	1.18	1.00	1.18	1.25
people:NNS	0.69	0.82	0.99	0.84	0.99	1.14
who:WP	1.11	0.97	1.39	1.30	1.39	1.55
are:VBP	0.70	1.32	1.02	0.91	1.02	1.14
Portugal:NNP	0.96	1.07	1.24	1.07	1.19	0.80
are:VBP	0.70	1.32	1.02	0.91	1.02	1.14
southern:NNP	0.62	0.82	0.99	0.82	1.87	1.07
Europe:NNP	0.94	1.05	1.24	1.11	1.24	0.28
NO_WORD	0.29	0.17	0.11	0.28	0.11	0.15

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.45 Alignment.score
1.00 0.24 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.17 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Europe" modifying "committee_members"
0.00 1.00 NegPolarity.hypNegWord : "few": is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : committee_members
-0.05 1.00 NullPunisher.aux : are
-0.10 1.00 NullPunisher.functionWord : There
-3.00 1.00 NullPunisher.entity : Europe
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
-1.00 1.00 Structure.relMismatch : hypothesis noun modifier "southern" and aligned text noun modifier "Europe" bear the same relation ("amod") but different parents ("Europe" vs. "Europe") hypothesis noun modifier "few" and aligned text noun modifier "committee_members" bear the same relation ("amod") but different parents ("committee_members" vs. "committee_members")
Hand-tuned score (dot product of above): -8.4799
Threshold: -9.4738

Inference ID: 029

Txt: Both commissioners used to be leading businessmen.

Hyp: Both commissioners used to be businessmen. (yes)

	Both DT	commissioners NNS	used VBD	to TO	be VB	businessmen NNS
Both:DT	2.42	1.00	1.25	1.31	1.25	1.00
commissioners:NNS	0.71	0.28	0.82	0.71	0.82	0.76
used:VBD	0.76	0.91	1.32	0.76	0.93	0.91
to:TO	1.31	1.00	1.25	2.42	1.25	1.00
be:VB	0.76	0.91	0.93	0.76	1.32	0.91
leading:VBG	0.76	0.91	0.63	0.76	0.81	0.80
businessmen:NNS	0.71	0.76	0.82	0.71	0.82	0.28
NO_WORD	0.82	0.28	0.17	1.55	1.43	0.28

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.66 Alignment.score
1.00 0.28 Alignment.isGood
-1.00 0.69 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : businessmen
-0.10 1.00 NullPunisher.functionWord : to
-1.00 1.00 NullPunisher.other : Both
-1.00 1.00 NullPunisher.other : used
-1.00 1.00 NullPunisher.other : commissioners
-0.05 1.00 NullPunisher.aux : be
-2.00 1.00 RootEntailment.unalignedRoot : "used" not aligned to anything
Hand-tuned score (dot product of above): -6.4921
Threshold: -9.4738

Inference ID: 030

Txt: Neither commissioner spends a lot of time at home.

Hyp: Neither commissioner spends time at home. (don't know)

	Neither DT	commissioner NN	spends VBZ	time NN	at_home IN
Neither:DT	2.42	0.98	1.25	0.98	1.05
commissioner:NN	0.69	0.28	0.77	0.87	0.50
spends:VBZ	0.76	0.86	1.32	0.59	1.07
a:DT	1.31	1.00	1.25	1.00	1.05
lot:NN	0.71	0.87	0.70	0.62	0.45
of:IN	1.05	1.31	1.24	1.31	1.05
time:NN	0.69	0.87	0.50	0.28	0.30
at_home:IN	1.05	1.31	1.15	1.11	1.59
NO_WORD	0.82	0.28	0.17	0.09	0.99

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.36 Alignment.score
1.00 0.23 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : spends
-1.00 1.00 NullPunisher.other : Neither
-1.00 1.00 NullPunisher.other : commissioner
-1.00 1.00 NullPunisher.other : time
-2.00 1.00 RootEntailment.unalignedRoot : "spends" not aligned to anything
Hand-tuned score (dot product of above): -7.6657
Threshold: -9.4738

Inference ID: 031

Txt: At least three commissioners spend a lot of time at home.

Hyp: At least three commissioners spend time at home. (yes)

	At IN	least JJS	three CD	commissioners NNS	spend VBP	time NN	at_home IN
At:IN	1.59	0.74	1.30	1.31	1.24	1.31	1.05
least:JJS	1.01	0.74	1.13	0.88	0.96	0.88	1.05
three:CD	1.30	1.00	0.85	1.18	1.22	1.01	1.23
commissioners:NNS	0.50	0.99	1.15	0.28	0.82	0.87	0.50
spend:VBP	1.15	1.02	1.31	0.91	1.32	0.51	1.07
a:DT	1.00	1.18	1.18	1.00	1.25	1.00	1.05
lot:NN	0.45	0.93	1.27	0.87	0.53	0.62	0.45
time:NN	0.50	0.99	0.98	0.87	0.42	0.28	0.30
at_home:IN	1.05	0.77	1.23	1.31	1.15	1.11	1.59
NO_WORD	0.84	0.29	0.52	0.28	0.17	0.09	1.16

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.30 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.29 Alignment.txtSpan
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : least
-1.00 1.00 NullPunisher.other : spend
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : at_home
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -7.5125
Threshold: -9.4738

Inference ID: 032

Txt: At most ten commissioners spend a lot of time at home.

Hyp: At most ten commissioners spend time at home. (don't know)

	At IN	most JJS	ten NN	commissioners NNS	spend VBP	time NN	at_home IN
At:IN	1.59	0.77	1.31	1.31	1.24	1.31	1.05
most:RBS	1.05	1.44	1.21	1.21	0.91	1.21	1.05
ten:NN	0.50	0.99	0.28	0.71	0.75	0.71	0.50
commissioners:NNS	0.50	0.99	0.71	0.28	0.82	0.87	0.50
spend:VBP	1.15	1.02	0.84	0.91	1.32	0.51	1.07
a:DT	1.00	1.18	1.00	1.00	1.25	1.00	1.05
lot:NN	0.45	0.89	0.76	0.87	0.53	0.62	0.45
time:NN	0.50	0.99	0.71	0.87	0.42	0.28	0.30
at_home:IN	1.05	0.77	1.31	1.31	1.15	1.11	1.59
NO_WORD	1.16	0.29	0.05	0.28	0.17	0.09	1.16

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.63 Alignment.score
1.00 0.28 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.43 Alignment.txtSpan
0.00 1.00 NegPolarity.hypNegWord : "most": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : spend
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -5.8497
Threshold: -9.4738

Inference ID: 033

Txt: An Irishman won a Nobel prize.

Hyp: An Irishman won the Nobel prize for literature. (don't know)

	An DT	Irishman NNP	won VBD	the DT	Nobel_prize NN	literature NN
An:DT	2.42	1.25	1.21	1.31	1.00	1.00
Irishman:NNP	0.96	0.28	1.07	0.96	1.07	1.07
won:VBD	0.71	1.16	1.32	0.76	0.57	0.91
a:DT	1.14	1.25	1.25	1.31	1.00	1.00
Nobel_prize:NN	0.71	1.07	0.49	0.71	0.28	0.38
NO_WORD	0.82	0.28	0.17	0.82	0.09	0.05

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.28 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "literature" modifying "won"
-1.00 1.00 NullPunisher.other : Nobel_prize
-1.00 1.00 NullPunisher.other : won
-1.00 1.00 NullPunisher.other : literature
-0.10 1.00 NullPunisher.article : An
-3.00 1.00 NullPunisher.entity : Irishman
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "won" not aligned to anything
Hand-tuned score (dot product of above): -10.0749
Threshold: -9.4738

Inference ID: 034

Txt: Every European can travel freely within Europe. Every European is a person. Every person who has the right to live in Europe can travel freely within Europe.

Hyp: Every European has the right to live in Europe. (don't know)

	Every DT	European NNP	has VBZ	the DT	right NN	to TO	live VB	Europe NNP
Every:DT	2.42	1.24	1.25	1.31	1.00	1.31	1.23	1.23
European:NNP	0.95	0.28	1.07	0.96	1.18	0.96	1.07	0.28
can:MD	1.31	1.25	1.43	1.31	1.00	1.31	1.37	1.25
travel:VB	0.74	1.13	0.94	0.73	0.89	0.76	0.59	1.13
freely:RB	1.03	1.44	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.94	0.28	1.07	0.96	1.17	0.96	1.07	0.28
Every:DT	2.42	1.24	1.25	1.31	1.00	1.31	1.23	1.23
European:NNP	0.95	0.28	1.07	0.96	1.18	0.96	1.07	0.28
is:VBZ	0.76	1.16	1.01	0.76	0.91	0.76	0.39	1.16
a:DT	1.31	1.25	1.25	1.31	1.00	1.31	1.25	1.25
person:NN	0.69	1.11	0.82	0.71	0.84	0.71	0.65	1.17
Every:DT	2.42	1.24	1.25	1.31	1.00	1.31	1.23	1.23
person:NN	0.69	1.11	0.82	0.71	0.84	0.71	0.65	1.17
who:WP	1.11	1.55	0.97	1.04	1.30	1.06	0.97	1.55
has:VBZ	0.76	1.16	1.32	0.76	0.91	0.76	0.75	1.16
the:DT	1.31	1.25	1.25	2.42	1.00	1.26	1.22	1.25
right:NN	0.71	1.18	0.82	0.71	0.28	0.71	0.79	1.17
to:TO	1.31	1.25	1.25	1.26	1.00	2.42	1.25	1.25
live:VB	0.73	1.16	0.75	0.73	0.88	0.76	1.32	1.16
Europe:NNP	0.94	0.28	1.07	0.96	1.17	0.96	1.07	0.28
can:MD	1.31	1.25	1.43	1.31	1.00	1.31	1.37	1.25
travel:VB	0.74	1.13	0.94	0.73	0.89	0.76	0.59	1.13
freely:RB	1.03	1.44	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.94	0.28	1.07	0.96	1.17	0.96	1.07	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.09	1.55	0.12	0.07

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.52 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.12 Alignment.txtSpan
-0.10 1.00 NullPunisher.functionWord : to
-1.00 1.00 NullPunisher.other : Every
-0.05 1.00 NullPunisher.aux : has
-1.00 1.00 NullPunisher.other : right
-1.00 1.00 NullPunisher.other : live
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "has" not aligned to anything
Hand-tuned score (dot product of above): -5.6849
Threshold: -9.4738

Inference ID: 035

Txt: All Europeans can travel freely within Europe. Every European is a person. Every person who has the right to live in Europe can travel freely within Europe.

Hyp: All Europeans have the right to live in Europe. (don't know)

	All DT	Europeans NNPS	have VBP	the DT	right NN	to TO	live VB	Europe NNP
All:DT	2.42	1.25	1.22	1.31	1.00	1.31	1.25	1.25
Europeans:NNPS	0.96	0.28	1.07	0.96	1.18	0.96	1.07	0.46
can:MD	1.31	1.25	1.47	1.31	1.00	1.31	1.37	1.25
travel:VB	0.73	1.15	0.27	0.73	0.89	0.76	0.59	1.13
freely:RB	1.09	1.46	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.96	0.46	1.07	0.96	1.17	0.96	1.07	0.28
Every:DT	1.31	1.25	1.23	1.31	1.00	1.31	1.23	1.23
European:NNP	0.96	1.37	1.07	0.96	1.18	0.96	1.07	0.28
is:VBZ	0.76	1.16	0.98	0.76	0.91	0.76	0.39	1.16
a:DT	1.31	1.25	1.25	1.31	1.00	1.31	1.25	1.25
person:NN	0.71	1.12	0.82	0.71	0.84	0.71	0.65	1.17
Every:DT	1.31	1.25	1.23	1.31	1.00	1.31	1.23	1.23
person:NN	0.71	1.12	0.82	0.71	0.84	0.71	0.65	1.17
who:WP	1.11	1.55	0.97	1.04	1.30	1.06	0.97	1.55
has:VBZ	0.76	1.16	0.19	0.76	0.91	0.76	0.75	1.16
the:DT	1.31	1.25	1.22	2.42	1.00	1.26	1.22	1.25
right:NN	0.71	1.18	0.82	0.71	0.28	0.71	0.79	1.17
to:TO	1.31	1.25	1.25	1.26	1.00	2.42	1.25	1.25
live:VB	0.76	1.16	0.64	0.73	0.88	0.76	1.32	1.16
Europe:NNP	0.96	0.46	1.07	0.96	1.17	0.96	1.07	0.28
can:MD	1.31	1.25	1.47	1.31	1.00	1.31	1.37	1.25
travel:VB	0.73	1.15	0.27	0.73	0.89	0.76	0.59	1.13
freely:RB	1.09	1.46	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.96	0.46	1.07	0.96	1.17	0.96	1.07	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.09	1.55	0.12	0.07

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.64 Alignment.score
1.00 0.28 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.38 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Europe" modifying "travel" is dropped on aligned hypothesis word "have"
-2.00 1.00 Modal.dontKnow : possible -> actual
-1.00 1.00 NullPunisher.other : All
-1.00 1.00 NullPunisher.other : live
-1.00 1.00 NullPunisher.other : right
-0.10 1.00 NullPunisher.article : the
-0.10 1.00 NullPunisher.functionWord : to
1.00 1.00 Quant.equivalent : Replacing the quantifier "every" by an equivalent quantifier "all" preserves truth.
-3.00 1.00 Structure.argsMismatch : args have different parents but same relations: text "European" <-nsubj-- "person" vs. hyp "Europeans" <-nsubj-- "have", which aligned to text "travel"
Hand-tuned score (dot product of above): -6.7407
Threshold: -9.4738

Inference ID: 036

Txt: Each European can travel freely within Europe. Every European is a person. Every person who has the right to live in Europe can travel freely within Europe.

Hyp: Each European has the right to live in Europe. (don't know)

	Each DT	European NNP	has VBZ	the DT	right NN	to TO	live VB	Europe NNP
Each:DT	2.42	1.00	1.48	1.56	1.23	1.56	1.51	1.17
European:NNP	0.71	0.28	1.07	0.96	1.18	0.96	1.07	0.28
can:MD	1.53	1.25	1.43	1.31	1.00	1.31	1.37	1.25
travel:VB	1.01	1.13	0.94	0.73	0.89	0.76	0.59	1.13
freely:RB	1.35	1.44	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.87	0.28	1.07	0.96	1.17	0.96	1.07	0.28
Every:DT	1.54	1.24	1.25	1.31	1.00	1.31	1.23	1.23
European:NNP	0.71	0.28	1.07	0.96	1.18	0.96	1.07	0.28
is:VBZ	1.01	1.16	1.01	0.76	0.91	0.76	0.39	1.16
a:DT	1.56	1.25	1.25	1.31	1.00	1.31	1.25	1.25
person:NN	0.96	1.11	0.82	0.71	0.84	0.71	0.65	1.17
Every:DT	1.54	1.24	1.25	1.31	1.00	1.31	1.23	1.23
person:NN	0.96	1.11	0.82	0.71	0.84	0.71	0.65	1.17
who:WP	1.37	1.55	0.97	1.04	1.30	1.06	0.97	1.55
has:VBZ	0.98	1.16	1.32	0.76	0.91	0.76	0.75	1.16
the:DT	1.56	1.25	1.25	2.42	1.00	1.26	1.22	1.25
right:NN	0.94	1.18	0.82	0.71	0.28	0.71	0.79	1.17
to:TO	1.56	1.25	1.25	1.26	1.00	2.42	1.25	1.25
live:VB	1.01	1.16	0.75	0.73	0.88	0.76	1.32	1.16
Europe:NNP	0.87	0.28	1.07	0.96	1.17	0.96	1.07	0.28
can:MD	1.53	1.25	1.43	1.31	1.00	1.31	1.37	1.25
travel:VB	1.01	1.13	0.94	0.73	0.89	0.76	0.59	1.13
freely:RB	1.35	1.44	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.87	0.28	1.07	0.96	1.17	0.96	1.07	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.09	1.55	0.12	0.07

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.52 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.12 Alignment.txtSpan
-0.10 1.00 NullPunisher.functionWord : to
-1.00 1.00 NullPunisher.other : live
-1.00 1.00 NullPunisher.other : right
-3.00 1.00 NullPunisher.entity : Each
-0.10 1.00 NullPunisher.article : the
-0.05 1.00 NullPunisher.aux : has
-2.00 1.00 RootEntailment.unalignedRoot : "has" not aligned to anything
Hand-tuned score (dot product of above): -7.6849
Threshold: -9.4738

Inference ID: 037

Txt: The residents of member states can travel freely within Europe. All residents of member states are individuals. Every individual who has the right to live anywhere in Europe can travel freely within Europe.

Hyp: The residents of member states have the right to live anywhere in Europe. (don't know)

	The DT	residents NNS	member NN	states NNS	have VBP	the DT	right NN	to TO	live VB	anywhere RB	Europe NNP
The:DT	2.42	1.00	1.00	0.97	1.22	0.01	1.00	1.26	1.22	1.45	1.25
residents:NNS	0.71	0.28	0.79	0.87	0.82	0.71	0.87	0.71	0.49	1.29	1.09
member:NN	0.71	0.79	0.28	0.80	0.82	0.71	0.93	0.71	0.82	1.27	1.13
states:NNS	0.68	0.87	0.80	0.28	0.77	0.68	0.99	0.71	0.71	1.31	1.15
can:MD	1.31	1.00	1.00	1.00	1.47	1.31	1.00	1.31	1.37	1.45	1.25
travel:VB	0.73	0.78	0.84	0.69	0.27	0.73	0.89	0.76	0.59	1.11	1.13
freely:RB	1.09	1.18	1.18	1.11	0.91	1.09	1.10	1.09	0.76	0.86	1.47
Europe:NNP	0.96	1.09	1.13	1.15	1.07	0.96	1.17	0.96	1.07	1.56	0.28
All:DT	1.31	1.00	1.00	1.00	1.22	1.31	1.00	1.31	1.25	1.45	1.25
residents:NNS	0.71	0.28	0.79	0.87	0.82	0.71	0.87	0.71	0.49	1.29	1.09
member:NN	0.71	0.79	0.28	0.80	0.82	0.71	0.93	0.71	0.82	1.27	1.13
states:NNS	0.68	0.87	0.80	0.28	0.77	0.68	0.99	0.71	0.71	1.31	1.15
are:VBP	0.68	0.91	0.91	0.88	0.89	0.68	0.91	0.76	0.36	1.09	1.14
individuals:NNS	0.71	0.74	0.85	0.94	0.82	0.71	1.02	0.71	0.80	1.31	1.21
Every:DT	1.31	1.00	0.94	1.00	1.23	1.31	1.00	1.31	1.23	1.44	1.23
individual:NN	0.71	0.86	0.88	0.99	0.82	0.71	0.95	0.71	0.82	1.31	1.21
who:WP	1.04	1.30	1.30	1.30	0.97	1.04	1.30	1.06	0.97	1.05	1.55
has:VBZ	0.76	0.91	0.91	0.88	0.19	0.76	0.91	0.76	0.75	1.11	1.16
the:DT	0.01	1.00	1.00	0.97	1.22	2.42	1.00	1.26	1.22	1.45	1.25
right:NN	0.71	0.87	0.93	0.99	0.82	0.71	0.28	0.71	0.79	1.31	1.17
to:TO	1.26	1.00	1.00	1.00	1.25	1.26	1.00	2.42	1.25	1.45	1.25
live:VB	0.73	0.58	0.91	0.80	0.64	0.73	0.88	0.76	1.32	1.11	1.16
anywhere:RB	1.09	1.20	1.18	1.21	0.91	1.09	1.21	1.09	0.91	1.23	1.47
Europe:NNP	0.96	1.09	1.13	1.15	1.07	0.96	1.17	0.96	1.07	1.56	0.28
can:MD	1.31	1.00	1.00	1.00	1.47	1.31	1.00	1.31	1.37	1.45	1.25
travel:VB	0.73	0.78	0.84	0.69	0.27	0.73	0.89	0.76	0.59	1.11	1.13
freely:RB	1.09	1.18	1.18	1.11	0.91	1.09	1.10	1.09	0.76	0.86	1.47
Europe:NNP	0.96	1.09	1.13	1.15	1.07	0.96	1.17	0.96	1.07	1.56	0.28
NO_WORD	0.82	0.28	0.05	0.04	0.17	0.82	0.09	1.55	0.12	0.04	0.07

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.47 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 4.00 Alignment.hypSpan
0.10 0.36 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Europe" modifying "live"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Europe" modifying "travel" is dropped on aligned hypothesis word "have"
-2.00 1.00 Modal.dontKnow : possible -> actual
-0.10 1.00 NullPunisher.functionWord : to
-1.00 1.00 NullPunisher.other : right
-0.10 1.00 NullPunisher.article : The
-1.00 1.00 NullPunisher.other : anywhere
-0.10 1.00 NullPunisher.article : the
-3.00 1.00 NullPunisher.entity : Europe
-1.00 1.00 NullPunisher.other : live
Hand-tuned score (dot product of above): -9.0804
Threshold: -9.4738

Inference ID: 038

Txt: No delegate finished the report.

Hyp: Some delegate finished the report on time. (don't know)

	Some DT	delegate NN	finished VBD	the DT	report NN	time NN
No:DT	1.31	1.00	1.25	1.31	1.00	1.00
delegate:NN	0.71	0.28	0.74	0.71	0.74	0.86
finished:VBD	0.76	0.83	1.32	0.76	0.86	0.77
the:DT	1.28	1.00	1.25	2.42	1.00	0.90
report:NN	0.71	0.74	0.77	0.71	0.28	0.91
NO_WORD	0.82	0.28	0.17	0.82	0.09	0.02

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.27 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "finished"
-1.00 1.00 NullPunisher.other : delegate
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : report
-1.00 1.00 NullPunisher.other : finished
-1.00 1.00 NullPunisher.other : Some
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -8.9897
Threshold: -9.4738

Inference ID: 039

Txt: Some delegates finished the survey.

Hyp: Some delegates finished the survey on time. (don't know)

	Some DT	delegates NNS	finished VBD	the DT	survey NN	time NN
Some:DT	2.42	1.00	1.25	1.28	0.95	0.89
delegates:NNS	0.71	0.28	0.78	0.71	0.53	0.86
finished:VBD	0.76	0.87	1.32	0.76	0.91	0.77
the:DT	1.28	1.00	1.25	2.42	1.00	0.90
survey:NN	0.66	0.53	0.82	0.71	0.28	0.84
NO_WORD	0.82	0.28	0.17	0.82	0.09	0.02

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.27 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "finished"
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : Some
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : finished
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : survey
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -8.9897
Threshold: -9.4738

Inference ID: 040

Txt: Many delegates obtained results from the survey.

Hyp: Many delegates obtained interesting results from the survey. (don't know)

	Many JJ	delegates NNS	obtained VBD	interesting JJ	results NNS	the DT	survey NN
Many:JJ	0.74	0.88	0.96	0.93	0.88	0.76	0.88
delegates:NNS	0.99	0.28	0.80	0.88	0.63	0.71	0.53
obtained:VBD	1.02	0.89	1.32	0.96	0.85	0.76	0.61
results:NNS	0.99	0.63	0.76	0.82	0.28	0.71	0.63
the:DT	1.18	1.00	1.25	1.18	1.00	2.42	1.00
survey:NN	0.99	0.53	0.52	0.85	0.63	0.71	0.28
NO_WORD	0.11	0.28	0.17	0.11	0.09	0.82	0.15

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.13 Alignment.score
1.00 0.19 Alignment.isGood
-1.00 0.79 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "survey" modifying "obtained"
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : results
-1.00 1.00 NullPunisher.other : survey
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : Many
-1.00 1.00 NullPunisher.other : obtained
-1.00 1.00 NullPunisher.other : interesting
-2.00 1.00 RootEntailment.unalignedRoot : "obtained" not aligned to anything
Hand-tuned score (dot product of above): -10.2775
Threshold: -9.4738

Inference ID: 041

Txt: Several delegates got the results published.

Hyp: Several delegates got the results published in major national newspapers. (don't know)

	Several JJ	delegates NNS	got VBD	the DT	results NNS	published VBN	major JJ	national JJ	newspapers NNS
Several:JJ	0.74	0.82	0.96	0.76	0.84	0.96	0.93	0.89	0.86
delegates:NNS	0.93	0.28	0.82	0.71	0.63	0.71	0.99	0.86	0.56
got:VBD	1.02	0.91	1.32	0.76	0.91	0.74	1.02	1.02	0.86
the:DT	1.18	1.00	1.25	2.42	1.00	1.25	1.18	1.18	1.00
results:NNS	0.95	0.63	0.82	0.71	0.28	0.64	0.99	0.99	0.82
published:VBD	1.02	0.80	0.74	0.76	0.73	2.02	1.01	0.89	0.65
NO_WORD	0.11	0.28	0.17	0.82	0.09	0.16	0.11	0.11	0.07

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.34 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.11 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "newspapers" modifying "published"
-1.00 1.00 NullPunisher.other : Several
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : national
-1.00 1.00 NullPunisher.other : got
-1.00 1.00 NullPunisher.other : major
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : results
-1.00 1.00 NullPunisher.other : newspapers
-2.00 1.00 RootEntailment.unalignedRoot : "got" not aligned to anything
Hand-tuned score (dot product of above): -10.9745
Threshold: -9.4738

Inference ID: 042

Txt: Most Europeans can travel freely within Europe. All Europeans are people. All people who are resident in Europe can travel freely within Europe.

Hyp: Most Europeans are resident in Europe. (don't know)

	Most JJS	Europeans NNPS	are VBP	resident VBN	Europe NNP
Most:JJS	0.74	1.13	0.96	0.96	1.13
Europeans:NNPS	1.24	0.28	1.07	1.03	0.46
can:MD	1.18	1.25	1.55	1.25	1.25
travel:VB	1.02	1.15	0.98	0.23	1.13
freely:RB	1.05	1.46	0.89	0.88	1.47
Europe:NNP	1.24	0.46	1.05	1.07	0.28
All:DT	1.18	1.25	1.18	1.25	1.25
Europeans:NNPS	1.24	0.28	1.07	1.03	0.46
are:VBP	1.02	1.16	1.32	0.56	1.14
people:NNS	0.99	1.21	0.82	0.60	1.14
All:DT	1.18	1.25	1.18	1.25	1.25
people:NNS	0.99	1.21	0.82	0.60	1.14
who:WP	1.39	1.55	0.97	0.97	1.55
are:VBP	1.02	1.16	1.32	0.56	1.14
resident:NN	0.99	0.89	0.82	2.71	1.09
Europe:NNP	1.24	0.46	1.05	1.07	0.28
can:MD	1.18	1.25	1.55	1.25	1.25
travel:VB	1.02	1.15	0.98	0.23	1.13
freely:RB	1.05	1.46	0.89	0.88	1.47
Europe:NNP	1.24	0.46	1.05	1.07	0.28
NO_WORD	0.11	0.15	1.57	0.17	0.07

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.99 Alignment.score
1.00 0.35 Alignment.isGood
-1.00 0.62 Alignment.isBad
-0.10 2.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.40 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Most" modifying "Europeans"
0.00 1.00 NegPolarity.hypNegWord : "Most": tag "JJS" is in NegPolarityMarkers list
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : Most
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
Hand-tuned score (dot product of above): 0.7143
Threshold: -9.4738

Inference ID: 043

Txt: A few committee members are from Scandinavia. All committee members are people. All people who are from Sweden are from Scandinavia.

Hyp: At least a few committee members are from Sweden. (don't know)

	At IN	least JJS	a DT	few JJ	committee_members NNS	are VBP	Sweden NNP
A:DT	1.00	1.18	0.13	1.18	1.00	1.25	1.25
few:JJ	1.05	0.93	0.76	0.74	0.88	0.96	1.13
committee_members:NNS	0.50	0.99	0.71	0.99	0.28	0.82	1.07
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
Scandinavia:NNP	0.75	1.24	0.96	1.24	1.06	1.07	0.76
All:DT	1.00	1.18	1.31	1.18	1.00	1.18	1.25
committee_members:NNS	0.50	0.99	0.71	0.99	0.28	0.82	1.07
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
people:NNS	0.50	0.97	0.71	0.99	0.84	0.82	1.16
All:DT	1.00	1.18	1.31	1.18	1.00	1.18	1.25
people:NNS	0.50	0.97	0.71	0.99	0.84	0.82	1.16
who:WP	1.05	1.39	1.11	1.39	1.30	0.97	1.55
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
Sweden:NNP	0.75	1.22	0.96	1.24	1.07	1.07	0.28
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
Scandinavia:NNP	0.75	1.24	0.96	1.24	1.06	1.07	0.76
NO_WORD	0.73	0.29	0.82	0.11	0.28	0.17	0.15

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.24 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Sweden" modifying "are"
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
0.00 1.00 NegPolarity.hypNegWord : "few": is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : least
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : few
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : committee_members
-3.00 1.00 NullPunisher.entity : Sweden
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -11.1707
Threshold: -9.4738

Inference ID: 044

Txt: Few committee members are from southern Europe. All committee members are people. All people who are from Portugal are from southern Europe.

Hyp: There are few committee members from Portugal. (yes)

	There EX	are VBP	few JJ	committee_members NNS	Portugal NNP
Few:JJ	0.76	0.96	0.76	0.88	1.13
committee_members:NNS	0.71	0.82	0.99	0.28	1.07
are:VBP	0.70	1.32	1.02	0.91	1.16
southern:JJ	0.68	0.96	0.93	0.88	1.08
Europe:NNP	0.94	1.05	1.24	1.11	0.80
All:DT	1.31	1.18	1.18	1.00	1.25
committee_members:NNS	0.71	0.82	0.99	0.28	1.07
are:VBP	0.70	1.32	1.02	0.91	1.16
people:NNS	0.69	0.82	0.99	0.84	1.14
All:DT	1.31	1.18	1.18	1.00	1.25
people:NNS	0.69	0.82	0.99	0.84	1.14
who:WP	1.11	0.97	1.39	1.30	1.55
are:VBP	0.70	1.32	1.02	0.91	1.16
Portugal:NNP	0.96	1.07	1.24	1.07	0.28
are:VBP	0.70	1.32	1.02	0.91	1.16
southern:NNP	0.62	0.82	0.99	0.82	1.02
Europe:NNP	0.94	1.05	1.24	1.11	0.80
NO_WORD	0.29	0.17	0.11	0.28	0.15

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.16 Alignment.score
1.00 0.19 Alignment.isGood
-1.00 0.79 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.20 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Portugal" modifying "committee_members"
0.00 1.00 NegPolarity.hypNegWord : "few": is in NegPolarityMarkers list
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : committee_members
-3.00 1.00 NullPunisher.entity : Portugal
-0.10 1.00 NullPunisher.functionWord : There
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
-1.00 1.00 Structure.relMismatch : hypothesis noun modifier "few" and aligned text noun modifier "committee_members" bear the same relation ("amod") but different parents ("committee_members" vs. "committee_members")
Hand-tuned score (dot product of above): -8.8627
Threshold: -9.4738

Inference ID: 045

Txt: Both commissioners used to be businessmen.

Hyp: Both commissioners used to be leading businessmen. (don't know)

	Both DT	commissioners NNS	used VBD	to TO	be VB	leading VBG	businessmen NNS
Both:DT	2.42	1.00	1.25	1.31	1.25	1.25	1.00
commissioners:NNS	0.71	0.28	0.82	0.71	0.82	0.82	0.76
used:VBD	0.76	0.91	1.32	0.76	0.93	0.63	0.91
to:TO	1.31	1.00	1.25	2.42	1.25	1.25	1.00
be:VB	0.76	0.91	0.93	0.76	1.32	0.89	0.91
businessmen:NNS	0.71	0.76	0.82	0.71	0.82	0.71	0.28
NO_WORD	0.82	0.28	0.17	1.55	1.43	0.12	0.28

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.59 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.71 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "leading" modifying "businessmen"
-1.00 1.00 NullPunisher.other : commissioners
-1.00 1.00 NullPunisher.other : leading
-0.05 1.00 NullPunisher.aux : be
-1.00 1.00 NullPunisher.other : used
-0.10 1.00 NullPunisher.functionWord : to
-1.00 1.00 NullPunisher.other : Both
-1.00 1.00 NullPunisher.other : businessmen
-2.00 1.00 RootEntailment.unalignedRoot : "used" not aligned to anything
Hand-tuned score (dot product of above): -8.7006
Threshold: -9.4738

Inference ID: 046

Txt: Neither commissioner spends time at home.

Hyp: One of the commissioners spends a lot of time at home. (don't know)

	One CD	the DT	commissioners NNS	spends VBZ	a DT	lot NN	of IN	time NN	at_home IN
Neither:DT	1.18	1.26	1.00	1.25	1.31	1.00	1.05	0.98	1.05
commissioner:NN	1.29	0.71	1.47	0.77	0.71	0.87	0.50	0.87	0.50
spends:VBZ	1.39	0.76	0.91	1.32	0.76	0.79	1.15	0.59	1.07
time:NN	1.26	0.61	0.87	0.50	0.71	0.62	0.50	0.28	0.30
at_home:IN	1.30	1.00	1.31	1.15	1.05	1.26	1.05	1.11	1.59
NO_WORD	0.79	0.82	0.04	0.17	0.82	0.09	1.16	0.25	0.99

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.55 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.71 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.11 Alignment.txtSpan
-3.00 1.00 NullPunisher.entity : One
-1.00 1.00 NullPunisher.other : lot
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : of
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : spends
-6.00 1.00 Numeric.mismatch : NUMBER mismatch: '1.0' vs ''
-2.00 1.00 RootEntailment.unalignedRoot : "spends" not aligned to anything
Hand-tuned score (dot product of above): -16.7851
Threshold: -9.4738

Inference ID: 047

Txt: At least three commissioners spend time at home.

Hyp: At least three commissioners spend a lot of time at home. (don't know)

	At IN	least JJS	three CD	commissioners NNS	spend VBP	a DT	lot NN	time NN	at_home IN
At:IN	1.59	0.74	1.30	1.31	1.24	1.00	1.27	1.31	1.05
least:JJS	1.01	0.74	1.13	0.88	0.96	0.76	0.82	0.88	1.05
three:CD	1.30	1.00	0.85	1.18	1.22	0.89	1.31	1.01	1.23
commissioners:NNS	0.50	0.99	1.15	0.28	0.82	0.71	0.87	0.87	0.50
spend:VBP	1.15	1.02	1.31	0.91	1.32	0.76	0.62	0.51	1.07
time:NN	0.50	0.99	0.98	0.87	0.42	0.71	0.62	0.28	0.30
at_home:IN	1.05	0.77	1.23	1.31	1.15	1.05	1.26	1.11	1.59
NO_WORD	0.84	0.29	0.52	0.28	0.17	0.82	0.09	0.04	0.99

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.31 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.22 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "lot"
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : spend
-1.00 1.00 NullPunisher.other : lot
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : least
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -9.8066
Threshold: -9.4738

Inference ID: 048

Txt: At most ten commissioners spend time at home.

Hyp: At most ten commissioners spend a lot of time at home. (yes)

	At IN	most RBS	ten NN	commissioners NNS	spend VBP	a DT	lot NN	time NN	at_home IN
At:IN	1.59	1.23	1.31	1.31	1.24	1.00	1.27	1.31	1.05
most:JJS	1.05	1.65	0.88	0.88	0.96	0.76	0.78	0.88	1.05
ten:NN	0.50	1.31	0.28	0.71	0.75	0.71	0.76	0.71	0.50
commissioners:NNS	0.50	1.31	0.71	0.28	0.82	0.71	0.87	0.87	0.50
spend:VBP	1.15	1.11	0.84	0.91	1.32	0.76	0.62	0.51	1.07
time:NN	0.50	1.31	0.71	0.87	0.42	0.71	0.62	0.28	0.30
at_home:IN	1.05	1.23	1.31	1.31	1.15	1.05	1.26	1.11	1.59
NO_WORD	0.99	0.04	0.05	0.28	0.17	0.82	0.09	0.04	0.99

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.57 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.71 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 3.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "lot"
0.00 1.00 NegPolarity.txtNegWord : "most": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : time
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : lot
-1.00 1.00 NullPunisher.other : spend
-1.00 1.00 NullPunisher.other : At
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -8.2409
Threshold: -9.4738

Inference ID: 049

Txt: A Swede won a Nobel prize. Every Swede is a Scandinavian.

Hyp: A Scandinavian won a Nobel prize. (yes)

	A DT	Scandinavian NNP	won VBD	a DT	Nobel_prize NN
A:DT	2.42	1.25	1.25	0.13	1.00
Swede:NN	0.96	0.56	1.07	0.96	1.07
won:VBD	0.76	1.16	1.32	0.76	0.57
a:DT	0.13	1.25	1.25	2.42	1.00
Nobel_prize:NN	0.71	1.06	0.49	0.71	0.28
Every:DT	1.31	1.25	1.25	1.31	1.00
Swede:NN	0.96	0.56	1.07	0.96	1.07
is:VBZ	0.76	1.16	0.88	0.76	0.91
a:DT	0.13	1.25	1.25	2.42	1.00
Scandinavian:NNP	0.96	0.28	1.07	0.96	1.06
NO_WORD	0.82	0.28	0.17	0.82	0.25

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.36 Alignment.score
1.00 0.23 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-3.00 1.00 NullPunisher.entity : Scandinavian
-1.00 1.00 NullPunisher.other : won
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : Nobel_prize
-0.10 1.00 NullPunisher.article : A
-2.00 1.00 RootEntailment.unalignedRoot : "won" not aligned to anything
Hand-tuned score (dot product of above): -7.8694
Threshold: -9.4738

Inference ID: 050

Txt: Every Canadian resident can travel freely within Europe. Every Canadian resident is a resident of the North American continent.

Hyp: Every resident of the North American continent can travel freely within Europe. (don't know)

	Every DT	resident NN	the DT	North_American NN	continent NN	can MD	travel VB	freely RB	Europe NNP
Every:DT	2.42	0.98	1.31	1.25	1.00	1.31	1.23	1.39	1.23
Canadian:JJ	1.02	1.11	1.02	0.86	1.07	1.00	1.22	1.27	1.05
resident:NN	0.69	0.28	0.71	0.91	0.65	0.71	0.49	1.27	1.09
can:MD	1.31	1.00	1.31	1.25	1.00	2.42	1.51	1.45	1.25
travel:VB	0.74	0.58	0.73	1.16	0.67	1.02	1.32	0.90	1.13
freely:RB	1.03	1.18	1.09	1.47	0.95	1.09	0.70	1.23	1.47
Europe:NNP	0.94	1.09	0.96	0.99	0.84	0.96	1.04	1.56	0.28
Every:DT	2.42	0.98	1.31	1.25	1.00	1.31	1.23	1.39	1.23
Canadian:JJ	1.02	1.11	1.02	0.86	1.07	1.00	1.22	1.27	1.05
resident:NN	0.69	0.28	0.71	0.91	0.65	0.71	0.49	1.27	1.09
is:VBZ	0.76	0.91	0.76	1.16	0.91	1.06	1.00	1.11	1.16
a:DT	1.31	1.00	1.31	1.25	1.00	1.31	1.25	1.45	1.25
resident:NN	0.69	0.28	0.71	0.91	0.65	0.71	0.49	1.27	1.09
the:DT	1.31	1.00	2.42	1.25	1.00	1.31	1.23	1.45	1.25
North_American:NNP	0.96	0.91	0.96	2.22	1.08	0.96	1.07	1.56	0.99
continent:NNP	0.71	0.65	0.71	1.08	3.06	0.71	0.58	1.04	0.93
NO_WORD	0.82	0.28	0.82	0.05	0.04	1.55	0.17	0.04	0.33

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.01 Alignment.score
1.00 0.36 Alignment.isGood
-1.00 0.61 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-1.00 1.00 NullPunisher.other : travel
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : freely
-1.00 1.00 NullPunisher.other : Every
-1.00 1.00 NullPunisher.other : resident
-0.05 1.00 NullPunisher.aux : can
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 1.2367
Threshold: -9.4738

Inference ID: 051

Txt: All Canadian residents can travel freely within Europe. Every Canadian resident is a resident of the North American continent.

Hyp: All residents of the North American continent can travel freely within Europe. (don't know)

	All DT	residents NNS	the DT	North_American NN	continent NN	can MD	travel VB	freely RB	Europe NNP
All:DT	2.42	1.00	1.31	1.25	1.00	1.31	1.23	1.45	1.25
Canadian:JJ	1.02	1.12	1.02	0.86	1.07	1.00	1.22	1.27	1.05
residents:NNS	0.71	0.28	0.71	0.91	0.67	0.71	0.69	1.27	1.09
can:MD	1.31	1.00	1.31	1.25	1.00	2.42	1.51	1.45	1.25
travel:VB	0.73	0.78	0.73	1.16	0.67	1.02	1.32	0.90	1.13
freely:RB	1.09	1.18	1.09	1.47	0.95	1.09	0.70	1.23	1.47
Europe:NNP	0.96	1.09	0.96	0.99	0.84	0.96	1.04	1.56	0.28
Every:DT	1.31	1.00	1.31	1.25	1.00	1.31	1.23	1.39	1.23
Canadian:JJ	1.02	1.12	1.02	0.86	1.07	1.00	1.22	1.27	1.05
resident:NN	0.71	1.32	0.71	0.91	0.65	0.71	0.49	1.27	1.09
is:VBZ	0.76	0.91	0.76	1.16	0.91	1.06	1.00	1.11	1.16
a:DT	1.31	1.00	1.31	1.25	1.00	1.31	1.25	1.45	1.25
resident:NN	0.71	1.32	0.71	0.91	0.65	0.71	0.49	1.27	1.09
the:DT	1.31	1.00	2.42	1.25	1.00	1.31	1.23	1.45	1.25
North_American:NNP	0.96	0.91	0.96	2.22	1.08	0.96	1.07	1.56	0.99
continent:NNP	0.71	0.67	0.71	1.08	3.06	0.71	0.58	1.04	0.93
NO_WORD	0.82	0.28	0.82	0.05	0.04	1.55	0.17	0.04	0.33

Response: yes (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 5.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.22 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-1.00 1.00 NullPunisher.other : freely
-1.00 1.00 NullPunisher.other : All
-0.05 1.00 NullPunisher.aux : can
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : travel
-7.00 1.00 Quant.expand : Invalid quantifier strengthening: replacing the quantifier "a" by a stronger quantifier "all" does NOT preserve truth.
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): -4.2912
Threshold: -9.4738

Inference ID: 052

Txt: Each Canadian resident can travel freely within Europe. Every Canadian resident is a resident of the North American continent.

Hyp: Each resident of the North American continent can travel freely within Europe. (don't know)

	Each DT	resident NN	the DT	North_American NN	continent NN	can MD	travel VB	freely RB	Europe NNP
Each:DT	2.42	1.00	1.31	1.25	1.00	1.28	1.25	1.45	1.25
Canadian:JJ	1.02	1.11	1.02	0.86	1.07	1.00	1.22	1.27	1.05
resident:NN	0.71	0.28	0.71	0.91	0.65	0.71	0.49	1.27	1.09
can:MD	1.28	1.00	1.31	1.25	1.00	2.42	1.51	1.45	1.25
travel:VB	0.76	0.58	0.73	1.16	0.67	1.02	1.32	0.90	1.13
freely:RB	1.09	1.18	1.09	1.47	0.95	1.09	0.70	1.23	1.47
Europe:NNP	0.96	1.09	0.96	0.99	0.84	0.96	1.04	1.56	0.28
Every:DT	1.28	0.98	1.31	1.25	1.00	1.31	1.23	1.39	1.23
Canadian:JJ	1.02	1.11	1.02	0.86	1.07	1.00	1.22	1.27	1.05
resident:NN	0.71	0.28	0.71	0.91	0.65	0.71	0.49	1.27	1.09
is:VBZ	0.76	0.91	0.76	1.16	0.91	1.06	1.00	1.11	1.16
a:DT	1.31	1.00	1.31	1.25	1.00	1.31	1.25	1.45	1.25
resident:NN	0.71	0.28	0.71	0.91	0.65	0.71	0.49	1.27	1.09
the:DT	1.31	1.00	2.42	1.25	1.00	1.31	1.23	1.45	1.25
North_American:NNP	0.96	0.91	0.96	2.22	1.08	0.96	1.07	1.56	0.99
continent:NNP	0.71	0.65	0.71	1.08	3.06	0.71	0.58	1.04	0.93
NO_WORD	0.82	0.28	0.82	0.05	0.04	1.55	0.17	0.04	0.33

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.01 Alignment.score
1.00 0.36 Alignment.isGood
-1.00 0.61 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-1.00 1.00 NullPunisher.other : freely
-0.05 1.00 NullPunisher.aux : can
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : resident
-1.00 1.00 NullPunisher.other : Each
-1.00 1.00 NullPunisher.other : travel
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 1.2367
Threshold: -9.4738

Inference ID: 053

Txt: The residents of major western countries can travel freely within Europe. All residents of major western countries are residents of western countries.

Hyp: The residents of western countries have the right to live in Europe. (don't know)

	The DT	residents NNS	western JJ	countries NNS	have VBP	the DT	right NN	to TO	live VB	Europe NNP
The:DT	2.42	1.00	1.18	1.00	1.22	0.01	1.00	1.26	1.22	1.25
residents:NNS	0.71	0.28	0.66	0.86	0.82	0.71	0.87	0.71	0.49	1.09
major:JJ	0.76	0.88	0.76	0.70	0.93	0.76	0.88	0.76	0.96	1.11
western:JJ	0.76	0.55	0.74	0.48	0.96	0.76	0.88	0.76	0.80	1.12
countries:NNS	0.71	0.86	0.59	0.28	0.82	0.71	0.95	0.71	0.73	1.17
can:MD	1.31	1.00	1.18	1.00	1.47	1.31	1.00	1.31	1.37	1.25
travel:VB	0.73	0.78	0.94	0.69	0.27	0.73	0.89	0.76	0.59	1.13
freely:RB	1.09	1.18	0.77	1.01	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.96	1.09	1.23	1.17	1.07	0.96	1.17	0.96	1.07	0.28
All:DT	1.31	1.00	1.18	1.00	1.22	1.31	1.00	1.31	1.25	1.25
residents:NNS	0.71	0.28	0.66	0.86	0.82	0.71	0.87	0.71	0.49	1.09
major:JJ	0.76	0.88	0.76	0.70	0.93	0.76	0.88	0.76	0.96	1.11
western:JJ	0.76	0.55	0.74	0.48	0.96	0.76	0.88	0.76	0.80	1.12
countries:NNS	0.71	0.86	0.59	0.28	0.82	0.71	0.95	0.71	0.73	1.17
are:VBP	0.68	0.91	1.02	0.91	0.89	0.68	0.91	0.76	0.36	1.14
residents:NNS	0.71	0.28	0.66	0.86	0.82	0.71	0.87	0.71	0.49	1.09
western:JJ	0.76	0.55	0.74	0.48	0.96	0.76	0.88	0.76	0.80	1.12
countries:NNS	0.71	0.86	0.59	0.28	0.82	0.71	0.95	0.71	0.73	1.17
NO_WORD	0.82	0.28	0.11	0.04	0.17	0.82	0.09	1.55	0.12	0.07

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.46 Alignment.score
1.00 0.24 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.20 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Europe" modifying "live"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Europe" modifying "travel" is dropped on aligned hypothesis word "have"
-2.00 1.00 Modal.dontKnow : possible -> actual
-0.10 1.00 NullPunisher.article : The
-0.10 1.00 NullPunisher.article : the
-3.00 1.00 NullPunisher.entity : Europe
-1.00 1.00 NullPunisher.other : live
-1.00 1.00 NullPunisher.other : western
-1.00 1.00 NullPunisher.other : right
-0.10 1.00 NullPunisher.functionWord : to
Hand-tuned score (dot product of above): -9.3122
Threshold: -9.4738

Inference ID: 054

Txt: No Scandinavian delegate finished the report on time.

Hyp: Some delegate finished the report on time. (don't know)

	Some DT	delegate NN	finished VBD	the DT	report NN	time NN
No:DT	1.31	1.00	1.25	1.31	1.00	1.00
Scandinavian:JJ	1.02	1.13	1.22	1.02	1.13	1.13
delegate:NN	0.71	0.28	0.74	0.71	0.74	0.86
finished:VBD	0.76	0.83	1.32	0.76	0.86	0.77
the:DT	1.28	1.00	1.25	2.42	1.00	0.90
report:NN	0.71	0.74	0.77	0.71	0.28	0.91
time:NN	0.60	0.86	0.68	0.61	0.91	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.09	0.02

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.27 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "finished"
-1.00 1.00 NullPunisher.other : Some
-1.00 1.00 NullPunisher.other : finished
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : delegate
-1.00 1.00 NullPunisher.other : report
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -8.9897
Threshold: -9.4738

Inference ID: 055

Txt: Some Irish delegates finished the survey on time.

Hyp: Some delegates finished the survey on time. (yes)

	Some DT	delegates NNS	finished VBD	the DT	survey NN	time NN
Some:DT	2.42	1.00	1.25	1.28	0.95	0.89
Irish:JJ	1.02	1.13	1.13	1.02	1.11	1.11
delegates:NNS	0.71	0.28	0.78	0.71	0.53	0.86
finished:VBD	0.76	0.87	1.32	0.76	0.91	0.77
the:DT	1.28	1.00	1.25	2.42	1.00	0.90
survey:NN	0.66	0.53	0.82	0.71	0.28	0.84
time:NN	0.60	0.86	0.68	0.61	0.84	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.09	0.02

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.27 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "finished"
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : finished
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : Some
-1.00 1.00 NullPunisher.other : survey
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -8.9897
Threshold: -9.4738

Inference ID: 056

Txt: Many British delegates obtained interesting results from the survey.

Hyp: Many delegates obtained interesting results from the survey. (don't know)

	Many JJ	delegates NNS	obtained VBD	interesting JJ	results NNS	the DT	survey NN
Many:JJ	0.74	0.88	0.96	0.93	0.88	0.76	0.88
British:JJ	1.19	1.13	1.20	1.17	1.13	1.02	1.13
delegates:NNS	0.99	0.28	0.80	0.88	0.63	0.71	0.53
obtained:VBD	1.02	0.89	1.32	0.96	0.85	0.76	0.61
interesting:JJ	0.93	0.77	0.90	0.74	0.71	0.76	0.74
results:NNS	0.99	0.63	0.76	0.82	0.28	0.71	0.63
the:DT	1.18	1.00	1.25	1.18	1.00	2.42	1.00
survey:NN	0.99	0.53	0.52	0.85	0.63	0.71	0.28
NO_WORD	0.11	0.28	0.17	0.11	0.09	0.82	0.15

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.13 Alignment.score
1.00 0.19 Alignment.isGood
-1.00 0.79 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "survey" modifying "obtained"
-1.00 1.00 NullPunisher.other : survey
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : interesting
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : Many
-1.00 1.00 NullPunisher.other : results
-1.00 1.00 NullPunisher.other : obtained
-2.00 1.00 RootEntailment.unalignedRoot : "obtained" not aligned to anything
Hand-tuned score (dot product of above): -10.2775
Threshold: -9.4738

Inference ID: 057

Txt: Several Portuguese delegates got the results published in major national newspapers.

Hyp: Several delegates got the results published in major national newspapers. (yes)

	Several JJ	delegates NNS	got VBD	the DT	results NNS	published VBN	major JJ	national JJ	newspapers NNS
Several:JJ	0.74	0.82	0.96	0.76	0.84	0.96	0.93	0.89	0.86
Portuguese:NNP	1.24	1.04	1.07	0.96	1.06	1.04	1.24	1.24	1.05
delegates:NNS	0.93	0.28	0.82	0.71	0.63	0.71	0.99	0.86	0.56
got:VBD	1.02	0.91	1.32	0.76	0.91	0.74	1.02	1.02	0.86
the:DT	1.18	1.00	1.25	2.42	1.00	1.25	1.18	1.18	1.00
results:NNS	0.95	0.63	0.82	0.71	0.28	0.64	0.99	0.99	0.82
published:VBN	1.02	0.80	0.74	0.76	0.73	1.32	1.01	0.89	0.65
major:JJ	0.93	0.88	0.96	0.76	0.88	0.95	0.74	0.63	0.73
national:JJ	0.89	0.75	0.96	0.76	0.88	0.83	0.63	0.74	0.62
newspapers:NNS	0.97	0.56	0.77	0.71	0.82	0.56	0.84	0.73	0.28
NO_WORD	0.11	0.28	0.17	0.82	0.09	0.16	0.11	0.11	0.07

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.14 Alignment.score
1.00 0.19 Alignment.isGood
-1.00 0.79 Alignment.isBad
-0.10 9.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "newspapers" modifying "published"
-1.00 1.00 NullPunisher.other : results
-1.00 1.00 NullPunisher.other : Several
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : national
-1.00 1.00 NullPunisher.other : newspapers
-1.00 1.00 NullPunisher.other : major
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : got
-1.00 1.00 NullPunisher.other : published
-2.00 1.00 RootEntailment.unalignedRoot : "got" not aligned to anything
Hand-tuned score (dot product of above): -12.4619
Threshold: -9.4738

Inference ID: 058

Txt: Most Europeans who are resident in Europe can travel freely within Europe.

Hyp: Most Europeans can travel freely within Europe. (don't know)

	Most JJS	Europeans NNPS	can MD	travel VB	freely RB	Europe NNP
Most:JJS	0.74	1.13	0.76	0.96	1.01	1.13
Europeans:NNS	1.24	2.22	0.96	1.06	1.55	0.46
who:WP	1.39	1.55	1.18	0.97	1.05	1.55
are:VBP	1.02	1.16	1.06	0.98	1.09	1.14
resident:NN	0.99	0.89	0.71	0.49	1.27	1.09
Europe:NNP	1.24	0.46	0.96	1.04	1.56	0.28
can:MD	1.18	1.25	2.42	1.51	1.45	1.25
travel:VB	1.02	1.15	1.02	1.32	0.90	1.13
freely:RB	1.05	1.46	1.09	0.70	1.23	1.47
Europe:NNP	1.24	0.46	0.96	1.04	1.56	0.28
NO_WORD	0.11	0.28	1.55	0.17	0.04	0.33

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.47 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.17 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "resident" modifying "Europeans" is dropped on aligned hypothesis word "Europe"
2.00 1.00 Modal.yes : actual -> possible
0.00 1.00 NegPolarity.hypNegWord : "Most": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : travel
-1.00 1.00 NullPunisher.other : freely
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : Most
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
Hand-tuned score (dot product of above): -3.8498
Threshold: -9.4738

Inference ID: 059

Txt: A few female committee members are from Scandinavia.

Hyp: At least a few committee members are from Scandinavia. (yes)

	At IN	least JJS	a DT	few JJ	committee_members NNS	are VBP	Scandinavia NNP
A:DT	1.00	1.18	0.13	1.18	1.00	1.25	1.25
few:JJ	1.05	0.93	0.76	0.74	0.88	0.96	1.13
female:JJ	1.05	0.87	0.76	0.91	0.59	0.93	1.13
committee_members:NNS	0.50	0.99	0.71	0.99	0.28	0.82	1.06
are:VBP	1.11	1.02	0.76	1.02	0.91	1.32	1.16
Scandinavia:NNP	0.75	1.24	0.96	1.24	1.06	1.07	0.28
NO_WORD	0.73	0.29	0.82	0.11	0.28	0.17	0.15

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.24 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Scandinavia" modifying "are"
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
0.00 1.00 NegPolarity.hypNegWord : "few": is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : committee_members
-3.00 1.00 NullPunisher.entity : Scandinavia
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : few
-1.00 1.00 NullPunisher.other : least
-0.10 1.00 NullPunisher.article : a
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -11.1707
Threshold: -9.4738

Inference ID: 060

Txt: Few female committee members are from southern Europe.

Hyp: Few committee members are from southern Europe. (don't know)

	Few JJ	committee_members NNS	are VBP	southern JJ	Europe NNP
Few:JJ	0.74	0.88	0.96	0.93	1.13
female:JJ	0.91	0.59	0.93	0.93	1.10
committee_members:NNS	0.99	0.28	0.82	0.99	1.11
are:VBP	1.02	0.91	1.32	1.02	1.14
southern:JJ	0.93	0.88	0.96	0.74	1.13
Europe:NNP	1.24	1.11	1.05	1.24	0.28
NO_WORD	0.11	0.28	0.17	0.11	0.15

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 -0.00 Alignment.score
1.00 0.17 Alignment.isGood
-1.00 0.81 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Europe" modifying "are"
-3.00 1.00 NullPunisher.entity : Europe
-1.00 1.00 NullPunisher.other : southern
-1.00 1.00 NullPunisher.other : Few
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : committee_members
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -10.1982
Threshold: -9.4738

Inference ID: 063

Txt: At least three female commissioners spend time at home.

Hyp: At least three commissioners spend time at home. (yes)

	At IN	least JJS	three CD	commissioners NNS	spend VBP	time NN	at_home IN
At:IN	1.59	0.74	1.30	1.31	1.24	1.31	1.05
least:JJS	1.01	0.74	1.13	0.88	0.96	0.88	1.05
three:CD	1.30	1.00	0.85	1.18	1.22	1.01	1.23
female:JJ	1.05	0.87	1.02	0.87	0.96	0.83	1.03
commissioners:NNS	0.50	0.99	1.15	0.28	0.82	0.87	0.50
spend:VBP	1.15	1.02	1.31	0.91	1.32	0.51	1.07
time:NN	0.50	0.99	0.98	0.87	0.42	0.28	0.30
at_home:IN	1.05	0.77	1.23	1.31	1.15	1.11	1.59
NO_WORD	0.84	0.29	0.52	0.28	0.17	0.09	1.16

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.30 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.29 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "female" modifying "commissioners" is dropped on aligned hypothesis word "commissioners"
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : least
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : spend
-1.00 1.00 NullPunisher.other : time
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -7.0125
Threshold: -9.4738

Inference ID: 064

Txt: At most ten female commissioners spend time at home.

Hyp: At most ten commissioners spend time at home. (don't know)

	At IN	most JJS	ten NN	commissioners NNS	spend VBP	time NN	at_home IN
most:JJS	1.05	0.74	0.88	0.88	0.96	0.88	1.05
ten:NN	0.50	0.99	0.28	0.71	0.75	0.71	0.50
female:JJ	1.05	0.93	0.88	0.87	0.96	0.83	1.03
commissioners:NNS	0.50	0.99	0.71	0.28	0.82	0.87	0.50
spend:VBP	1.15	1.02	0.84	0.91	1.32	0.51	1.07
time:NN	0.50	0.99	0.71	0.87	0.42	0.28	0.30
at_home:IN	1.05	0.77	1.31	1.31	1.15	1.11	1.59
NO_WORD	1.16	0.29	0.05	0.28	0.17	0.09	1.16

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.36 Alignment.score
1.00 0.23 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "ten" modifying "commissioners"
0.00 1.00 NegPolarity.hypNegWord : "most": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : spend
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : ten
-1.00 1.00 NullPunisher.other : commissioners
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : most
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -10.8594
Threshold: -9.4738

Inference ID: 065

Txt: A Scandinavian won a Nobel prize. Every Swede is a Scandinavian.

Hyp: A Swede won a Nobel prize. (don't know)

	A DT	Swede NN	won VBD	a DT	Nobel_prize NN
A:DT	2.42	1.25	1.25	0.13	1.00
Scandinavian:NNP	0.96	0.56	1.07	0.96	1.06
won:VBD	0.76	1.16	1.32	0.76	0.57
a:DT	0.13	1.25	1.25	2.42	1.00
Nobel_prize:NN	0.71	1.07	0.49	0.71	0.28
Every:DT	1.31	1.21	1.25	1.31	1.00
Swede:NN	0.96	0.28	1.07	0.96	1.07
is:VBZ	0.76	1.16	0.88	0.76	0.91
a:DT	0.13	1.25	1.25	2.42	1.00
Scandinavian:NNP	0.96	0.56	1.07	0.96	1.06
NO_WORD	0.82	0.28	0.17	0.82	0.25

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.36 Alignment.score
1.00 0.23 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : Nobel_prize
-3.00 1.00 NullPunisher.entity : Swede
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : won
-0.10 1.00 NullPunisher.article : A
-2.00 1.00 RootEntailment.unalignedRoot : "won" not aligned to anything
Hand-tuned score (dot product of above): -7.8694
Threshold: -9.4738

Inference ID: 066

Txt: Every resident of the North American continent can travel freely within Europe. Every Canadian resident is a resident of the North American continent.

Hyp: Every Canadian resident can travel freely within Europe. (yes)

	Every DT	Canadian JJ	resident NN	can MD	travel VB	freely RB	Europe NNP
Every:DT	2.42	1.44	0.98	1.31	1.23	1.39	1.23
resident:NN	0.69	1.22	0.28	0.71	0.49	1.27	1.09
the:DT	1.31	1.44	1.00	1.31	1.23	1.45	1.25
North_American:NN	0.96	0.97	0.91	0.96	1.07	1.56	0.99
continent:NN	0.71	1.18	0.65	0.71	0.58	1.04	0.93
can:MD	1.31	1.42	1.00	2.42	1.51	1.45	1.25
travel:VB	0.74	1.28	0.58	1.02	1.32	0.90	1.13
freely:RB	1.03	1.30	1.18	1.09	0.70	1.23	1.47
Europe:NNP	0.94	1.15	1.09	0.96	1.04	1.56	0.28
Every:DT	2.42	1.44	0.98	1.31	1.23	1.39	1.23
Canadian:JJ	1.02	0.74	1.11	1.00	1.22	1.27	1.05
resident:NN	0.69	1.22	0.28	0.71	0.49	1.27	1.09
is:VBZ	0.76	1.28	0.91	1.06	1.00	1.11	1.16
a:DT	1.31	1.44	1.00	1.31	1.25	1.45	1.25
resident:NN	0.69	1.22	0.28	0.71	0.49	1.27	1.09
the:DT	1.31	1.44	1.00	1.31	1.23	1.45	1.25
North_American:NNP	0.96	0.97	0.91	0.96	1.07	1.56	0.99
continent:NNP	0.71	1.18	0.65	0.71	0.58	1.04	0.93
NO_WORD	0.82	0.11	0.28	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.31 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : freely
-3.00 1.00 NullPunisher.entity : Canadian
-1.00 1.00 NullPunisher.other : resident
-1.00 1.00 NullPunisher.other : travel
-1.00 1.00 NullPunisher.other : Every
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): -2.7729
Threshold: -9.4738

Inference ID: 067

Txt: All residents of the North American continent can travel freely within Europe. Every Canadian resident is a resident of the North American continent.

Hyp: All Canadian residents can travel freely within Europe. (yes)

	All DT	Canadian JJ	residents NNS	can MD	travel VB	freely RB	Europe NNP
All:DT	2.42	1.44	1.00	1.31	1.23	1.45	1.25
residents:NNS	0.71	1.23	0.28	0.71	0.69	1.27	1.09
the:DT	1.31	1.44	1.00	1.31	1.23	1.45	1.25
North_American:NN	0.96	0.97	0.91	0.96	1.07	1.56	0.99
continent:NN	0.71	1.18	0.67	0.71	0.58	1.04	0.93
can:MD	1.31	1.42	1.00	2.42	1.51	1.45	1.25
travel:VB	0.73	1.28	0.78	1.02	1.32	0.90	1.13
freely:RB	1.09	1.30	1.18	1.09	0.70	1.23	1.47
Europe:NNP	0.96	1.15	1.09	0.96	1.04	1.56	0.28
Every:DT	1.31	1.44	1.00	1.31	1.23	1.39	1.23
Canadian:JJ	1.02	0.74	1.12	1.00	1.22	1.27	1.05
resident:NN	0.71	1.22	1.32	0.71	0.49	1.27	1.09
is:VBZ	0.76	1.28	0.91	1.06	1.00	1.11	1.16
a:DT	1.31	1.44	1.00	1.31	1.25	1.45	1.25
resident:NN	0.71	1.22	1.32	0.71	0.49	1.27	1.09
the:DT	1.31	1.44	1.00	1.31	1.23	1.45	1.25
North_American:NNP	0.96	0.97	0.91	0.96	1.07	1.56	0.99
continent:NNP	0.71	1.18	0.67	0.71	0.58	1.04	0.93
NO_WORD	0.82	0.11	0.28	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.53 Alignment.score
1.00 0.26 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.29 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Canadian" modifying "resident" is dropped on aligned hypothesis word "residents"
2.00 1.00 Modal.yes : actual -> possible
-1.00 1.00 NullPunisher.other : travel
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : freely
-1.00 1.00 NullPunisher.other : All
-3.00 1.00 NullPunisher.entity : Canadian
1.00 1.00 Quant.equivalent : Replacing the quantifier "every" by an equivalent quantifier "all" preserves truth.
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): 0.1542
Threshold: -9.4738

Inference ID: 068

Txt: Each resident of the North American continent can travel freely within Europe. Every Canadian resident is a resident of the North American continent.

Hyp: Each Canadian resident can travel freely within Europe. (yes)

	Each DT	Canadian JJ	resident NN	can MD	travel VB	freely RB	Europe NNP
Each:DT	2.42	1.44	1.00	1.28	1.25	1.45	1.25
resident:NN	0.71	1.22	0.28	0.71	0.49	1.27	1.09
the:DT	1.31	1.44	1.00	1.31	1.23	1.45	1.25
North_American:NN	0.96	0.97	0.91	0.96	1.07	1.56	0.99
continent:NN	0.71	1.18	0.65	0.71	0.58	1.04	0.93
can:MD	1.28	1.42	1.00	2.42	1.51	1.45	1.25
travel:VB	0.76	1.28	0.58	1.02	1.32	0.90	1.13
freely:RB	1.09	1.30	1.18	1.09	0.70	1.23	1.47
Europe:NNP	0.96	1.15	1.09	0.96	1.04	1.56	0.28
Every:DT	1.28	1.44	0.98	1.31	1.23	1.39	1.23
Canadian:JJ	1.02	0.74	1.11	1.00	1.22	1.27	1.05
resident:NN	0.71	1.22	0.28	0.71	0.49	1.27	1.09
is:VBZ	0.76	1.28	0.91	1.06	1.00	1.11	1.16
a:DT	1.31	1.44	1.00	1.31	1.25	1.45	1.25
resident:NN	0.71	1.22	0.28	0.71	0.49	1.27	1.09
the:DT	1.31	1.44	1.00	1.31	1.23	1.45	1.25
North_American:NNP	0.96	0.97	0.91	0.96	1.07	1.56	0.99
continent:NNP	0.71	1.18	0.65	0.71	0.58	1.04	0.93
NO_WORD	0.82	0.11	0.28	1.55	0.17	0.04	0.33

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.31 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.76 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
2.00 1.00 Modal.yes : actual -> possible
-0.05 1.00 NullPunisher.aux : can
-1.00 1.00 NullPunisher.other : travel
-1.00 1.00 NullPunisher.other : resident
-1.00 1.00 NullPunisher.other : freely
-1.00 1.00 NullPunisher.other : Each
-3.00 1.00 NullPunisher.entity : Canadian
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): -2.7729
Threshold: -9.4738

Inference ID: 069

Txt: The residents of western countries can travel freely within Europe. All residents of major western countries are residents of western countries.

Hyp: The residents of major western countries have the right to live in Europe. (yes)

	The DT	residents NNS	major JJ	western JJ	countries NNS	have VBP	the DT	right NN	to TO	live VB	Europe NNP
The:DT	2.42	1.00	1.18	1.18	1.00	1.22	0.01	1.00	1.26	1.22	1.25
residents:NNS	0.71	0.28	0.99	0.66	0.86	0.82	0.71	0.87	0.71	0.49	1.09
western:JJ	0.76	0.55	0.76	0.74	0.48	0.96	0.76	0.88	0.76	0.80	1.12
countries:NNS	0.71	0.86	0.81	0.59	0.28	0.82	0.71	0.95	0.71	0.73	1.17
can:MD	1.31	1.00	1.18	1.18	1.00	1.47	1.31	1.00	1.31	1.37	1.25
travel:VB	0.73	0.78	0.91	0.94	0.69	0.27	0.73	0.89	0.76	0.59	1.13
freely:RB	1.09	1.18	0.99	0.77	1.01	0.91	1.09	1.10	1.09	0.76	1.47
Europe:NNP	0.96	1.09	1.22	1.23	1.17	1.07	0.96	1.17	0.96	1.07	0.28
All:DT	1.31	1.00	1.18	1.18	1.00	1.22	1.31	1.00	1.31	1.25	1.25
residents:NNS	0.71	0.28	0.99	0.66	0.86	0.82	0.71	0.87	0.71	0.49	1.09
major:JJ	0.76	0.88	0.74	0.76	0.70	0.93	0.76	0.88	0.76	0.96	1.11
western:JJ	0.76	0.55	0.76	0.74	0.48	0.96	0.76	0.88	0.76	0.80	1.12
countries:NNS	0.71	0.86	0.81	0.59	0.28	0.82	0.71	0.95	0.71	0.73	1.17
are:VBP	0.68	0.91	1.02	1.02	0.91	0.89	0.68	0.91	0.76	0.36	1.14
residents:NNS	0.71	0.28	0.99	0.66	0.86	0.82	0.71	0.87	0.71	0.49	1.09
western:JJ	0.76	0.55	0.76	0.74	0.48	0.96	0.76	0.88	0.76	0.80	1.12
countries:NNS	0.71	0.86	0.81	0.59	0.28	0.82	0.71	0.95	0.71	0.73	1.17
NO_WORD	0.82	0.28	0.11	0.11	0.04	0.17	0.82	0.09	1.55	0.12	0.07

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.42 Alignment.score
1.00 0.24 Alignment.isGood
-1.00 0.74 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.18 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Europe" modifying "live"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Europe" modifying "travel" is dropped on aligned hypothesis word "have"
-2.00 1.00 Modal.dontKnow : possible -> actual
-0.10 1.00 NullPunisher.article : The
-1.00 1.00 NullPunisher.other : western
-1.00 1.00 NullPunisher.other : live
-1.00 1.00 NullPunisher.other : right
-0.10 1.00 NullPunisher.functionWord : to
-3.00 1.00 NullPunisher.entity : Europe
-1.00 1.00 NullPunisher.other : major
-0.10 1.00 NullPunisher.article : the
Hand-tuned score (dot product of above): -10.4568
Threshold: -9.4738

Inference ID: 070

Txt: No delegate finished the report on time.

Hyp: Some Scandinavian delegate finished the report on time. (don't know)

	Some DT	Scandinavian JJ	delegate NN	finished VBD	the DT	report NN	time NN
No:DT	1.31	1.44	1.00	1.25	1.31	1.00	1.00
delegate:NN	0.71	1.24	0.28	0.74	0.71	0.74	0.86
finished:VBD	0.76	1.28	0.83	1.32	0.76	0.86	0.77
the:DT	1.28	1.44	1.00	1.25	2.42	1.00	0.90
report:NN	0.71	1.24	0.74	0.77	0.71	0.28	0.91
time:NN	0.60	1.24	0.86	0.68	0.61	0.91	0.28
NO_WORD	0.82	0.11	0.28	0.17	0.82	0.09	0.02

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.25 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "finished"
-1.00 1.00 NullPunisher.other : finished
-1.00 1.00 NullPunisher.other : report
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : delegate
-1.00 1.00 NullPunisher.other : Some
-1.00 1.00 NullPunisher.other : time
-3.00 1.00 NullPunisher.entity : Scandinavian
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -12.1191
Threshold: -9.4738

Inference ID: 071

Txt: Some delegates finished the survey on time.

Hyp: Some Irish delegates finished the survey on time. (don't know)

	Some DT	Irish JJ	delegates NNS	finished VBD	the DT	survey NN	time NN
Some:DT	2.42	1.44	1.00	1.25	1.28	0.95	0.89
delegates:NNS	0.71	1.24	0.28	0.78	0.71	0.53	0.86
finished:VBD	0.76	1.19	0.87	1.32	0.76	0.91	0.77
the:DT	1.28	1.44	1.00	1.25	2.42	1.00	0.90
survey:NN	0.66	1.22	0.53	0.82	0.71	0.28	0.84
time:NN	0.60	1.22	0.86	0.68	0.61	0.84	0.28
NO_WORD	0.82	0.11	0.28	0.17	0.82	0.09	0.02

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.25 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "time" modifying "finished"
-1.00 1.00 NullPunisher.other : finished
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : time
-3.00 1.00 NullPunisher.entity : Irish
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : Some
-1.00 1.00 NullPunisher.other : survey
-2.00 1.00 RootEntailment.unalignedRoot : "finished" not aligned to anything
Hand-tuned score (dot product of above): -12.1191
Threshold: -9.4738

Inference ID: 072

Txt: Many delegates obtained interesting results from the survey.

Hyp: Many British delegates obtained interesting results from the survey. (don't know)

	Many JJ	British JJ	delegates NNS	obtained VBD	interesting JJ	results NNS	the DT	survey NN
Many:JJ	0.74	1.19	0.88	0.96	0.93	0.88	0.76	0.88
delegates:NNS	0.99	1.24	0.28	0.80	0.88	0.63	0.71	0.53
obtained:VBD	1.02	1.26	0.89	1.32	0.96	0.85	0.76	0.61
interesting:JJ	0.93	1.17	0.77	0.90	0.74	0.71	0.76	0.74
results:NNS	0.99	1.24	0.63	0.76	0.82	0.28	0.71	0.63
the:DT	1.18	1.44	1.00	1.25	1.18	1.00	2.42	1.00
survey:NN	0.99	1.24	0.53	0.52	0.85	0.63	0.71	0.28
NO_WORD	0.11	0.11	0.28	0.17	0.11	0.09	0.82	0.15

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.12 Alignment.score
1.00 0.19 Alignment.isGood
-1.00 0.79 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "survey" modifying "obtained"
-1.00 1.00 NullPunisher.other : obtained
-3.00 1.00 NullPunisher.entity : British
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : Many
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : interesting
-1.00 1.00 NullPunisher.other : survey
-1.00 1.00 NullPunisher.other : results
-2.00 1.00 RootEntailment.unalignedRoot : "obtained" not aligned to anything
Hand-tuned score (dot product of above): -13.3794
Threshold: -9.4738

Inference ID: 073

Txt: Several delegates got the results published in major national newspapers.

Hyp: Several Portuguese delegates got the results published in major national newspapers. (don't know)

	Several JJ	Portuguese NNP	delegates NNS	got VBD	the DT	results NNS	published VBN	major JJ	national JJ	newspapers NNS
Several:JJ	0.74	1.13	0.82	0.96	0.76	0.84	0.96	0.93	0.89	0.86
delegates:NNS	0.93	1.04	0.28	0.82	0.71	0.63	0.71	0.99	0.86	0.56
got:VBD	1.02	1.16	0.91	1.32	0.76	0.91	0.74	1.02	1.02	0.86
the:DT	1.18	1.25	1.00	1.25	2.42	1.00	1.25	1.18	1.18	1.00
results:NNS	0.95	1.06	0.63	0.82	0.71	0.28	0.64	0.99	0.99	0.82
published:VBN	1.02	1.13	0.80	0.74	0.76	0.73	1.32	1.01	0.89	0.65
major:JJ	0.93	1.13	0.88	0.96	0.76	0.88	0.95	0.74	0.63	0.73
national:JJ	0.89	1.13	0.75	0.96	0.76	0.88	0.83	0.63	0.74	0.62
newspapers:NNS	0.97	1.05	0.56	0.77	0.71	0.82	0.56	0.84	0.73	0.28
NO_WORD	0.11	0.05	0.28	0.17	0.82	0.09	0.16	0.11	0.11	0.07

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.12 Alignment.score
1.00 0.19 Alignment.isGood
-1.00 0.79 Alignment.isBad
-0.10 10.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "newspapers" modifying "published"
-1.00 1.00 NullPunisher.other : national
-1.00 1.00 NullPunisher.other : got
-1.00 1.00 NullPunisher.other : results
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : newspapers
-1.00 1.00 NullPunisher.other : major
-3.00 1.00 NullPunisher.entity : Portuguese
-1.00 1.00 NullPunisher.other : published
-1.00 1.00 NullPunisher.other : delegates
-1.00 1.00 NullPunisher.other : Several
-2.00 1.00 RootEntailment.unalignedRoot : "got" not aligned to anything
Hand-tuned score (dot product of above): -15.5872
Threshold: -9.4738

Inference ID: 074

Txt: Most Europeans can travel freely within Europe.

Hyp: Most Europeans who are resident outside Europe can travel freely within Europe. (don't know)

	Most JJS	Europeans NNS	who WP	are VBP	resident NN	Europe NNP	can MD	travel VB	freely RB	Europe NNP
Most:JJS	0.74	1.13	1.05	0.96	0.88	1.13	0.76	0.96	1.01	1.13
Europeans:NNPS	1.24	2.22	1.84	1.07	0.89	0.46	0.96	1.06	1.55	0.46
can:MD	1.18	1.25	1.24	1.55	1.00	1.25	2.42	1.51	1.45	1.25
travel:VB	1.02	1.15	0.54	0.98	0.58	1.13	1.02	1.32	0.90	1.13
freely:RB	1.05	1.46	1.05	0.89	1.18	1.47	1.09	0.70	1.23	1.47
Europe:NNP	1.24	0.46	1.84	1.05	1.09	0.28	0.96	1.04	1.56	0.28
NO_WORD	0.11	0.28	0.92	1.43	0.19	0.10	1.55	0.17	0.04	0.33

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.65 Alignment.score
1.00 0.28 Alignment.isGood
-1.00 0.69 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.10 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "freely" modifying "travel"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Most" modifying "Europeans" is dropped on aligned hypothesis word "Europeans"
2.00 1.00 Modal.yes : actual -> possible
0.00 1.00 NegPolarity.hypNegWord : "Most": tag "JJS" is in NegPolarityMarkers list
-0.05 1.00 NullPunisher.aux : can
-3.00 1.00 NullPunisher.entity : Europe
-1.00 1.00 NullPunisher.other : Most
-0.10 1.00 NullPunisher.functionWord : who
-1.00 1.00 NullPunisher.other : resident
-1.00 1.00 NullPunisher.other : travel
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : freely
-2.00 1.00 RootEntailment.unalignedRoot : "travel" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Europe"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): -2.1558
Threshold: -9.4738

Inference ID: 075

Txt: A few committee members are from Scandinavia.

Hyp: At least a few female committee members are from Scandinavia. (don't know)

	At IN	least JJS	a DT	few JJ	female JJ	committee_members NNS	are VBP	Scandinavia NNP
A:DT	1.00	1.18	0.13	1.18	1.18	1.00	1.25	1.25
few:JJ	1.05	0.93	0.76	0.74	0.91	0.88	0.96	1.13
committee_members:NNS	0.50	0.99	0.71	0.99	0.70	0.28	0.82	1.06
are:VBP	1.11	1.02	0.76	1.02	0.99	0.91	1.32	1.16
Scandinavia:NNP	0.75	1.24	0.96	1.24	1.24	1.06	1.07	0.28
NO_WORD	0.73	0.29	0.82	0.11	0.11	0.28	0.17	0.15

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.23 Alignment.score
1.00 0.20 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Scandinavia" modifying "are"
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
0.00 1.00 NegPolarity.hypNegWord : "few": is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : female
-1.00 1.00 NullPunisher.other : At
-0.05 1.00 NullPunisher.aux : are
-3.00 1.00 NullPunisher.entity : Scandinavia
-1.00 1.00 NullPunisher.other : committee_members
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : few
-1.00 1.00 NullPunisher.other : least
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -12.2924
Threshold: -9.4738

Inference ID: 076

Txt: Few committee members are from southern Europe.

Hyp: Few female committee members are from southern Europe. (yes)

	Few JJ	female JJ	committee_members NNS	are VBP	southern JJ	Europe NNP
Few:JJ	0.74	0.91	0.88	0.96	0.93	1.13
committee_members:NNS	0.99	0.70	0.28	0.82	0.99	1.11
are:VBP	1.02	0.99	0.91	1.32	1.02	1.14
southern:JJ	0.93	0.93	0.88	0.96	0.74	1.13
Europe:NNP	1.24	1.21	1.11	1.05	1.24	0.28
NO_WORD	0.11	0.11	0.28	0.17	0.11	0.15

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.01 Alignment.score
1.00 0.17 Alignment.isGood
-1.00 0.81 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Europe" modifying "are"
-1.00 1.00 NullPunisher.other : Few
-0.05 1.00 NullPunisher.aux : are
-1.00 1.00 NullPunisher.other : committee_members
-1.00 1.00 NullPunisher.other : female
-3.00 1.00 NullPunisher.entity : Europe
-1.00 1.00 NullPunisher.other : southern
-2.00 1.00 RootEntailment.unalignedRoot : "are" not aligned to anything
Hand-tuned score (dot product of above): -11.2726
Threshold: -9.4738

Inference ID: 079

Txt: At least three commissioners spend time at home.

Hyp: At least three male commissioners spend time at home. (don't know)

	At IN	least JJS	three CD	male JJ	commissioners NNS	spend VBP	time NN	at_home IN
At:IN	1.59	0.74	1.30	0.77	1.31	1.24	1.31	1.05
least:JJS	1.01	0.74	1.13	0.91	0.88	0.96	0.88	1.05
three:CD	1.30	1.00	0.85	0.91	1.18	1.22	1.01	1.23
commissioners:NNS	0.50	0.99	1.15	0.99	0.28	0.82	0.87	0.50
spend:VBP	1.15	1.02	1.31	1.02	0.91	1.32	0.51	1.07
time:NN	0.50	0.99	0.98	0.93	0.87	0.42	0.28	0.30
at_home:IN	1.05	0.77	1.23	0.77	1.31	1.15	1.11	1.59
NO_WORD	0.84	0.29	0.52	0.11	0.28	0.17	0.09	1.16

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.28 Alignment.score
1.00 0.21 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.25 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "male" modifying "commissioners"
0.00 1.00 NegPolarity.hypNegWord : "least": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : male
-1.00 1.00 NullPunisher.other : least
-1.00 1.00 NullPunisher.other : At
-1.00 1.00 NullPunisher.other : spend
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : at_home
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -9.7480
Threshold: -9.4738

Inference ID: 080

Txt: At most ten commissioners spend time at home.

Hyp: At most ten female commissioners spend time at home. (yes)

	most JJS	ten NN	female JJ	commissioners NNS	spend VBP	time NN	at_home IN
At:IN	0.77	1.31	0.77	1.31	1.24	1.31	1.05
most:JJS	0.74	0.88	0.93	0.88	0.96	0.88	1.05
ten:NN	0.99	0.28	0.98	0.71	0.75	0.71	0.50
commissioners:NNS	0.99	0.71	0.98	0.28	0.82	0.87	0.50
spend:VBP	1.02	0.84	1.02	0.91	1.32	0.51	1.07
time:NN	0.99	0.71	0.94	0.87	0.42	0.28	0.30
at_home:IN	0.77	1.31	0.75	1.31	1.15	1.11	1.59
NO_WORD	0.11	0.20	0.11	0.28	0.17	0.09	1.16

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.22 Alignment.score
1.00 0.20 Alignment.isGood
-1.00 0.78 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "female" modifying "commissioners"
0.00 1.00 NegPolarity.hypNegWord : "most": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : commissioners
-1.00 1.00 NullPunisher.other : time
-1.00 1.00 NullPunisher.other : ten
-1.00 1.00 NullPunisher.other : most
-1.00 1.00 NullPunisher.other : at_home
-1.00 1.00 NullPunisher.other : female
-1.00 1.00 NullPunisher.other : spend
-2.00 1.00 RootEntailment.unalignedRoot : "spend" not aligned to anything
Hand-tuned score (dot product of above): -11.0461
Threshold: -9.4738

Inference ID: 081

Txt: Smith, Jones and Anderson signed the contract.

Hyp: Jones signed the contract. (yes)

	Jones NNP	signed VBD	the DT	contract NN
Smith:NNP	0.70	1.05	0.96	1.16
Jones:NNP	0.28	1.01	0.96	1.16
Anderson:NNP	0.75	1.07	0.96	1.10
signed:VBD	1.10	1.32	0.76	0.45
the:DT	1.25	1.25	2.42	1.00
contract:NN	1.16	0.36	0.71	0.28
NO_WORD	0.28	0.17	0.82	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.20 Alignment.score
1.00 0.20 Alignment.isGood
-1.00 0.78 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-3.00 1.00 NullPunisher.entity : Jones
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : contract
-1.00 1.00 NullPunisher.other : signed
-2.00 1.00 RootEntailment.unalignedRoot : "signed" not aligned to anything
Hand-tuned score (dot product of above): -7.8790
Threshold: -9.4738

Inference ID: 082

Txt: Smith, Jones and several lawyers signed the contract.

Hyp: Jones signed the contract. (yes)

	Jones NNP	signed VBD	the DT	contract NN
Smith:NNP	0.70	1.05	0.96	1.16
Jones:NNP	0.28	1.01	0.96	1.16
several:JJ	1.13	0.94	0.76	0.83
lawyers:NNS	1.03	0.62	0.71	0.84
signed:VBD	1.10	1.32	0.76	0.45
the:DT	1.25	1.25	2.42	1.00
contract:NN	1.16	0.36	0.71	0.28
NO_WORD	0.28	0.17	0.82	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.20 Alignment.score
1.00 0.20 Alignment.isGood
-1.00 0.78 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-3.00 1.00 NullPunisher.entity : Jones
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : signed
-1.00 1.00 NullPunisher.other : contract
-2.00 1.00 RootEntailment.unalignedRoot : "signed" not aligned to anything
Hand-tuned score (dot product of above): -7.8790
Threshold: -9.4738

Inference ID: 083

Txt: Either Smith, Jones or Anderson signed the contract.

Hyp: Jones signed the contract. (don't know)

	Jones NNP	signed VBD	the DT	contract NN
Either:CC	1.23	1.18	1.23	1.00
Smith:NNP	0.70	1.05	0.96	1.16
Jones:NNP	0.28	1.01	0.96	1.16
Anderson:NNP	0.75	1.07	0.96	1.10
signed:VBD	1.10	1.32	0.76	0.45
the:DT	1.25	1.25	2.42	1.00
contract:NN	1.16	0.36	0.71	0.28
NO_WORD	0.28	0.17	0.82	0.09

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.20 Alignment.score
1.00 0.20 Alignment.isGood
-1.00 0.78 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : signed
-1.00 1.00 NullPunisher.other : contract
-3.00 1.00 NullPunisher.entity : Jones
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "signed" not aligned to anything
Hand-tuned score (dot product of above): -7.8790
Threshold: -9.4738

Inference ID: 084

Txt: Either Smith, Jones or Anderson signed the contract.

Hyp: If Smith and Anderson did not sign the contract, Jones signed the contract. (yes)

If
IN

Smith
NNP

Anderson
NNP

did
VBD

not
RB

sign
VB

the
DT

contract
NN

Jones
NNP

signed
VBD

the
DT

contract
NN

Either:CC

1.05

1.19

1.25

1.45

1.25

1.23

1.00

1.23

1.18

1.23

1.00

Smith:NNP

0.75

0.28

0.76

1.07

1.56

1.00

0.96

1.16

0.70

1.05

0.96

1.16

Jones:NNP

0.75

0.70

0.75

1.07

1.56

1.07

0.96

1.16

0.28

1.01

0.96

1.16

Anderson:NNP

0.75

0.76

0.28

1.07

1.56

1.07

0.96

1.10

0.75

1.07

0.96

1.10

signed:VBD

1.15

1.14

1.16

0.63

1.11

0.93

0.76

0.45

1.10

1.32

0.76

0.45

the:DT

1.05

1.25

1.45

1.25

2.42

1.00

1.25

2.42

1.00

contract:NN

0.50

1.16

1.10

0.71

1.31

0.57

0.71

0.28

1.16

0.36

0.71

0.28

NO_WORD

0.92

0.28

0.01

1.30

0.24

0.42

0.82

0.09

0.28

0.17

0.82

0.09

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.50 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.72 Alignment.isBad
-0.10 9.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.17 Alignment.txtSpan
0.00 1.00 NegPolarity.hypNegWord : "sign": has child with relation "neg"
-1.00 1.00 NullPunisher.other : signed
-0.10 1.00 NullPunisher.article : the
-0.05 1.00 NullPunisher.aux : did
-1.00 1.00 NullPunisher.other : contract
-3.00 1.00 NullPunisher.entity : Anderson
-0.10 1.00 NullPunisher.article : the
-3.00 1.00 NullPunisher.entity : Jones
-1.00 1.00 NullPunisher.other : not
-1.00 1.00 NullPunisher.other : If
1.00 1.00 Quant.contract : Valid quantifier weakening: replacing the quantifier "the" by a weaker quantifier "the" preserves truth.
-2.00 1.00 RootEntailment.unalignedRoot : "signed" not aligned to anything
Hand-tuned score (dot product of above): -12.0113
Threshold: -9.4738

Inference ID: 085

Txt: Exactly two lawyers and three accountants signed the contract.

Hyp: Six lawyers signed the contract. (don't know)

	Six CD	lawyers NNS	signed VBD	the DT	contract NN
Exactly:RB	1.36	1.21	0.91	1.09	1.20
two:CD	0.35	1.32	1.17	0.82	1.25
lawyers:NNS	1.29	0.28	0.62	0.71	0.84
three:CD	0.35	1.33	1.26	0.78	1.32
accountants:NNS	1.29	0.37	0.82	0.71	0.81
signed:VBD	1.37	0.70	1.32	0.76	0.45
the:DT	1.18	1.00	1.25	2.42	1.00
contract:NN	1.29	0.84	0.36	0.71	0.28
NO_WORD	0.52	0.28	0.17	0.82	0.09

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.23 Alignment.score
1.00 0.20 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 3.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.40 Alignment.txtSpan
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : contract
-1.00 1.00 NullPunisher.other : signed
-6.00 1.00 Numeric.mismatch : NUMBER mismatch: '6.0' vs '2.0'
-2.00 1.00 RootEntailment.unalignedRoot : "signed" not aligned to anything
Hand-tuned score (dot product of above): -10.5021
Threshold: -9.4738

Inference ID: 086

Txt: Exactly two lawyers and three accountants signed the contract.

Hyp: Six accountants signed the contract. (don't know)

	Six CD	accountants NNS	signed VBD	the DT	contract NN
Exactly:RB	1.36	1.21	0.91	1.09	1.20
two:CD	0.35	1.21	1.17	0.82	1.25
lawyers:NNS	1.29	0.37	0.62	0.71	0.84
three:CD	0.35	1.24	1.26	0.78	1.32
accountants:NNS	1.29	0.28	0.82	0.71	0.81
signed:VBD	1.37	0.91	1.32	0.76	0.45
the:DT	1.18	1.00	1.25	2.42	1.00
contract:NN	1.29	0.81	0.36	0.71	0.28
NO_WORD	0.52	0.28	0.17	0.82	0.09

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.23 Alignment.score
1.00 0.20 Alignment.isGood
-1.00 0.77 Alignment.isBad
-0.10 3.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.40 Alignment.txtSpan
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : signed
-1.00 1.00 NullPunisher.other : contract
-6.00 1.00 Numeric.mismatch : NUMBER mismatch: '6.0' vs '3.0'
-2.00 1.00 RootEntailment.unalignedRoot : "signed" not aligned to anything
Hand-tuned score (dot product of above): -10.5021
Threshold: -9.4738

Inference ID: 087

Txt: Every representative and client was at the meeting.

Hyp: Every representative was at the meeting. (yes)

	Every DT	representative NN	was VBD	the DT	meeting NN
Every:DT	2.42	1.00	1.25	1.31	1.00
representative:NN	0.71	0.28	0.82	0.71	0.82
client:NN	0.69	0.84	0.82	0.71	0.89
was:VBD	0.76	0.91	1.32	0.76	0.91
the:DT	1.31	1.00	1.25	2.42	1.00
meeting:NN	0.71	0.82	0.82	0.71	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.20

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.35 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "meeting" modifying "was"
-1.00 1.00 NullPunisher.other : meeting
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : Every
-0.05 1.00 NullPunisher.aux : was
-1.00 1.00 NullPunisher.other : representative
-2.00 1.00 RootEntailment.unalignedRoot : "was" not aligned to anything
Hand-tuned score (dot product of above): -6.8325
Threshold: -9.4738

Inference ID: 088

Txt: Every representative and client was at the meeting.

Hyp: Every representative was at the meeting. (don't know)

	Every DT	representative NN	was VBD	the DT	meeting NN
Every:DT	2.42	1.00	1.25	1.31	1.00
representative:NN	0.71	0.28	0.82	0.71	0.82
client:NN	0.69	0.84	0.82	0.71	0.89
was:VBD	0.76	0.91	1.32	0.76	0.91
the:DT	1.31	1.00	1.25	2.42	1.00
meeting:NN	0.71	0.82	0.82	0.71	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.20

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.35 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "meeting" modifying "was"
-1.00 1.00 NullPunisher.other : representative
-1.00 1.00 NullPunisher.other : meeting
-1.00 1.00 NullPunisher.other : Every
-0.05 1.00 NullPunisher.aux : was
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "was" not aligned to anything
Hand-tuned score (dot product of above): -6.8325
Threshold: -9.4738

Inference ID: 089

Txt: Every representative or client was at the meeting.

Hyp: Every representative and every client was at the meeting. (yes)

	Every DT	representative NN	every DT	client NN	was VBD	the DT	meeting NN
Every:DT	2.42	1.00	0.22	0.98	1.25	1.31	1.00
representative:NN	0.71	0.28	0.71	0.84	0.82	0.71	0.82
client:NN	0.69	0.84	0.69	0.28	0.82	0.71	0.89
was:VBD	0.76	0.91	0.76	0.91	1.32	0.76	0.91
the:DT	1.31	1.00	1.31	1.00	1.25	2.42	1.00
meeting:NN	0.71	0.82	0.71	0.89	0.82	0.71	0.28
NO_WORD	0.82	0.28	0.82	0.01	0.17	0.82	0.20

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.36 Alignment.score
1.00 0.23 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 7.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "meeting" modifying "was"
-1.00 1.00 NullPunisher.other : client
-1.00 1.00 NullPunisher.other : meeting
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : Every
-0.05 1.00 NullPunisher.aux : was
-1.00 1.00 NullPunisher.other : every
-1.00 1.00 NullPunisher.other : representative
-2.00 1.00 RootEntailment.unalignedRoot : "was" not aligned to anything
Hand-tuned score (dot product of above): -9.0074
Threshold: -9.4738

Inference ID: 090

Txt: The chairman read out the items on the agenda.

Hyp: The chairman read out every item on the agenda. (yes)

	The DT	chairman NN	read VB	every DT	item NN	the DT	agenda NN
The:DT	2.42	1.00	1.25	1.31	0.96	0.01	1.00
chairman:NN	0.71	0.28	0.82	0.71	0.89	0.71	0.75
read:VB	0.76	0.91	1.32	0.73	0.66	0.76	0.81
the:DT	0.01	1.00	1.25	1.31	0.96	2.42	1.00
items:NNS	0.71	0.96	0.72	0.66	1.06	0.71	0.84
the:DT	0.01	1.00	1.25	1.31	0.96	2.42	1.00
agenda:NN	0.71	0.75	0.72	0.69	0.78	0.71	0.28
NO_WORD	0.82	0.28	0.17	0.82	0.62	0.82	0.02

Inference ID: 091

Txt: The people who were at the meeting voted for a new chairman.

Hyp: Everyone at the meeting voted for a new chairman. (don't know)

	Everyone NN	the DT	meeting NN	voted VBD	a DT	new JJ	chairman NN
The:DT	1.00	0.01	1.00	1.25	1.31	1.18	1.00
people:NNS	0.79	0.71	0.86	0.80	0.71	0.99	1.02
who:WP	1.30	1.04	1.30	0.97	1.11	1.39	1.30
were:VBD	0.87	0.73	0.91	0.76	0.76	0.99	0.91
the:DT	1.00	2.42	1.00	1.25	1.31	1.18	1.00
meeting:NN	0.77	0.71	0.28	0.54	0.71	0.99	0.67
voted:VBN	0.89	0.76	0.63	2.10	0.76	0.99	0.55
a:DT	1.00	1.31	1.00	1.25	2.42	1.18	1.00
new:JJ	0.88	0.76	0.88	0.93	0.76	0.74	0.87
chairman:NN	0.79	0.71	0.67	0.46	0.71	0.98	0.28
NO_WORD	0.28	0.82	0.20	0.17	0.82	0.11	0.05

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.60 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "new" modifying "chairman"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "new" modifying "chairman" is dropped on aligned hypothesis word "chairman"
-0.10 1.00 NullPunisher.article : the
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : meeting
-1.00 1.00 NullPunisher.other : Everyone
-1.00 1.00 NullPunisher.other : new
Hand-tuned score (dot product of above): -3.9137
Threshold: -9.4738

Inference ID: 092

Txt: All the people who were at the meeting voted for a new chairman.

Hyp: Everyone at the meeting voted for a new chairman. (yes)

	Everyone NN	the DT	meeting NN	voted VBD	a DT	new JJ	chairman NN
All:PDT	1.00	1.31	1.00	1.25	1.31	1.18	1.00
the:DT	1.00	2.42	1.00	1.25	1.31	1.18	1.00
people:NNS	0.79	0.71	0.86	0.80	0.71	0.99	1.02
who:WP	1.30	1.04	1.30	0.97	1.11	1.39	1.30
were:VBD	0.87	0.73	0.91	0.76	0.76	0.99	0.91
the:DT	1.00	2.42	1.00	1.25	1.31	1.18	1.00
meeting:NN	0.77	0.71	0.28	0.54	0.71	0.99	0.67
voted:VBN	0.89	0.76	0.63	2.10	0.76	0.99	0.55
a:DT	1.00	1.31	1.00	1.25	2.42	1.18	1.00
new:JJ	0.88	0.76	0.88	0.93	0.76	0.74	0.87
chairman:NN	0.79	0.71	0.67	0.46	0.71	0.98	0.28
NO_WORD	0.28	0.82	0.20	0.17	0.82	0.11	0.05

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.60 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "new" modifying "chairman"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "new" modifying "chairman" is dropped on aligned hypothesis word "chairman"
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : Everyone
-1.00 1.00 NullPunisher.other : new
-1.00 1.00 NullPunisher.other : meeting
-0.10 1.00 NullPunisher.article : a
Hand-tuned score (dot product of above): -3.9137
Threshold: -9.4738

Inference ID: 093

Txt: The people who were at the meeting all voted for a new chairman.

Hyp: Everyone at the meeting voted for a new chairman. (yes)

	Everyone NN	the DT	meeting NN	voted VBD	a DT	new JJ	chairman NN
The:DT	1.00	0.01	1.00	1.25	1.31	1.18	1.00
people:NNS	0.79	0.71	0.86	0.80	0.71	0.99	1.02
who:WP	1.30	1.04	1.30	0.97	1.11	1.39	1.30
were:VBD	0.87	0.73	0.91	0.76	0.76	0.99	0.91
the:DT	1.00	2.42	1.00	1.25	1.31	1.18	1.00
meeting:NN	0.77	0.71	0.28	0.54	0.71	0.99	0.67
all:DT	1.00	1.31	1.00	1.25	1.31	1.18	1.00
voted:VBN	0.89	0.76	0.63	2.10	0.76	0.99	0.55
a:DT	1.00	1.31	1.00	1.25	2.42	1.18	1.00
new:JJ	0.88	0.76	0.88	0.93	0.76	0.74	0.87
chairman:NN	0.79	0.71	0.67	0.46	0.71	0.98	0.28
NO_WORD	0.28	0.82	0.20	0.17	0.82	0.11	0.05

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.60 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.70 Alignment.isBad
-0.10 5.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.14 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "new" modifying "chairman"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "new" modifying "chairman" is dropped on aligned hypothesis word "chairman"
-1.00 1.00 NullPunisher.other : new
-1.00 1.00 NullPunisher.other : meeting
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : Everyone
-0.10 1.00 NullPunisher.article : the
Hand-tuned score (dot product of above): -3.9137
Threshold: -9.4738

Inference ID: 094

Txt: The inhabitants of Cambridge voted for a Labour MP.

Hyp: Every inhabitant of Cambridge voted for a Labour MP. (don't know)

	Every DT	inhabitant NN	Cambridge NNP	voted VBD	a DT	Labour_MP NNP
The:DT	1.31	1.00	1.25	1.25	1.31	1.25
inhabitants:NNS	0.71	1.17	1.08	0.82	0.71	1.15
Cambridge:NNP	0.96	1.07	0.28	1.07	0.96	0.99
voted:VBD	0.76	0.91	1.16	1.32	0.76	1.16
a:DT	1.31	1.00	1.25	1.25	2.42	1.25
Labour_MP:NNP	0.96	1.16	0.99	1.07	0.96	0.28
NO_WORD	0.82	0.28	0.04	0.17	0.82	0.05

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.58 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.71 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "Labour_MP" modifying "voted"
-1.00 1.00 NullPunisher.other : voted
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : Every
-3.00 1.00 NullPunisher.entity : Labour_MP
-7.00 1.00 Quant.expand : Invalid quantifier strengthening: replacing the quantifier "the" by a stronger quantifier "every" does NOT preserve truth.
-2.00 1.00 RootEntailment.unalignedRoot : "voted" not aligned to anything
2.00 1.00 Location.match : Locations match between txt and hyp; both have lemma "Cambridge"
4.00 1.00 Location.match&OK.Root.poorlyOrUnAligned :
Hand-tuned score (dot product of above): -9.1256
Threshold: -9.4738

Inference ID: 095

Txt: The Ancient Greeks were noted philosophers.

Hyp: Every Ancient Greek was a noted philosopher. (don't know)

	Every DT	Ancient_Greek NNP	was VBD	a DT	noted JJ	philosopher NN
The:DT	1.31	1.25	1.25	1.31	1.18	1.00
Ancient:NNP	0.96	0.39	1.07	0.96	1.21	1.00
Greeks:NNP	0.94	0.49	1.07	0.96	1.22	1.10
were:VBD	0.68	1.16	0.13	0.76	0.99	0.91
noted:VBN	0.76	1.16	0.86	0.76	2.37	0.88
philosophers:NNS	0.71	1.06	0.82	0.71	0.93	1.46
NO_WORD	0.82	0.28	1.43	0.82	0.11	0.12

Response: yes (INCORRECT)
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.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.50 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "Ancient" modifying "Greeks" is dropped on aligned hypothesis word "Ancient_Greek"
-1.00 1.00 Apposition.mismatch : no apposition in text between philosopher and Ancient_Greek
-1.00 1.00 Hypernym.posNarrow : Narrowing a term (from "Greeks" to "Ancient_Greek") does NOT preserve truth in a positive context
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : Every
-0.05 1.00 NullPunisher.aux : was
-7.00 1.00 Quant.expand : Invalid quantifier strengthening: replacing the quantifier "the" by a stronger quantifier "every" does NOT preserve truth.
Hand-tuned score (dot product of above): -8.4827
Threshold: -9.4738

Inference ID: 096

Txt: The Ancient Greeks were all noted philosophers.

Hyp: Every Ancient Greek was a noted philosopher. (yes)

	Every DT	Ancient_Greek NNP	was VBD	a DT	noted JJ	philosopher NN
The:DT	1.31	1.25	1.25	1.31	1.18	1.00
Ancient:NNP	0.96	0.39	1.07	0.96	1.21	1.00
Greeks:NNP	0.94	0.49	1.07	0.96	1.22	1.10
were:VBD	0.68	1.16	0.13	0.76	0.99	0.91
all:RB	1.09	1.47	0.91	1.09	1.05	1.21
noted:VBN	0.76	1.16	0.86	0.76	2.37	0.88
philosophers:NNS	0.71	1.06	0.82	0.71	0.93	1.46
NO_WORD	0.82	0.28	1.43	0.82	0.11	0.12

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.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "all" modifying "noted" is dropped on aligned hypothesis word "noted"
-1.00 1.00 Apposition.mismatch : no apposition in text between philosopher and Ancient_Greek
-1.00 1.00 Hypernym.posNarrow : Narrowing a term (from "Greeks" to "Ancient_Greek") does NOT preserve truth in a positive context
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : Every
-0.05 1.00 NullPunisher.aux : was
-7.00 1.00 Quant.expand : Invalid quantifier strengthening: replacing the quantifier "the" by a stronger quantifier "every" does NOT preserve truth.
Hand-tuned score (dot product of above): -8.4993
Threshold: -9.4738

Inference ID: 097

Txt: Software faults were blamed for the system failure.

Hyp: The system failure was blamed on one or more software faults. (yes)

	The DT	system NN	failure NN	was VBD	blamed VBN	one CD	or CC	more JJR	software NN	faults NNS
Software:NNP	0.71	0.94	0.89	0.82	0.82	1.29	0.71	0.95	1.67	0.92
faults:NNS	0.71	0.72	0.27	0.79	0.82	1.29	0.71	0.99	0.82	0.28
were:VBD	0.73	0.91	0.89	0.13	0.76	1.36	0.76	0.91	0.87	0.91
blamed:VBN	0.76	0.76	0.50	0.76	1.32	1.39	0.76	1.02	0.91	0.91
the:DT	0.01	1.00	1.00	1.25	1.25	1.11	1.31	1.15	1.00	1.00
system:NN	0.71	0.28	0.52	0.82	0.67	1.29	0.71	0.99	0.49	0.72
failure:NN	0.71	0.52	0.28	0.82	0.41	1.29	0.71	0.97	0.86	0.27
NO_WORD	0.82	0.05	0.15	1.57	0.17	0.52	0.56	0.15	0.05	0.02

Response: dontknow (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 0.42 Alignment.score
1.00 0.24 Alignment.isGood
-1.00 0.74 Alignment.isBad
-0.10 9.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.10 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "faults" modifying "blamed"
-1.00 1.00 NullPunisher.other : faults
-1.00 1.00 NullPunisher.other : blamed
-0.10 1.00 NullPunisher.article : The
-3.00 1.00 NullPunisher.entity : one
-1.00 1.00 NullPunisher.other : or
-0.05 1.00 NullPunisher.aux : was
-1.00 1.00 NullPunisher.other : failure
-1.00 1.00 NullPunisher.other : system
-1.00 1.00 NullPunisher.other : more
-6.00 1.00 Numeric.mismatch : NUMBER mismatch: '>=1.0' vs ''
-2.00 1.00 RootEntailment.unalignedRoot : "blamed" not aligned to anything
Hand-tuned score (dot product of above): -19.0219
Threshold: -9.4738

Inference ID: 098

Txt: Software faults were blamed for the system failure. Bug # 32-985 is a known software fault.

Hyp: Bug # 32-985 was blamed for the system failure. (don't know)

	Bug NN	# #	32-985 CD	was VBD	blamed VBN	the DT	system NN	failure NN
Software:NNP	0.88	0.96	1.29	0.82	0.82	0.71	0.94	0.89
faults:NNS	0.85	0.96	1.29	0.79	0.82	0.71	0.72	0.27
were:VBD	0.91	1.01	1.39	0.13	0.76	0.73	0.91	0.89
blamed:VBN	0.91	0.96	1.39	0.76	1.32	0.76	0.76	0.50
the:DT	1.00	1.56	1.18	1.25	1.25	2.42	1.00	1.00
system:NN	0.83	0.96	1.29	0.82	0.67	0.71	0.28	0.52
failure:NN	0.87	0.96	1.29	0.82	0.41	0.71	0.52	0.28
Bug:NN	0.28	0.96	1.29	0.82	0.82	0.71	0.83	0.87
#:#	1.25	2.42	0.75	1.51	1.46	1.56	1.25	1.25
32-985:CD	1.33	0.46	0.85	1.30	1.30	0.89	1.32	1.33
is:VBZ	0.91	1.01	1.39	0.05	0.76	0.76	0.91	0.91
a:DT	1.00	1.56	1.18	1.25	1.25	1.31	1.00	1.00
known:JJ	0.88	1.02	1.13	0.96	0.86	0.76	0.67	0.62
software:NN	0.88	0.96	1.29	0.82	0.82	0.71	0.49	0.86
fault:NN	0.85	0.96	1.25	0.82	0.53	0.71	0.73	0.06
NO_WORD	0.15	0.56	0.26	1.57	0.17	0.82	0.05	0.05

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.34 Alignment.score
1.00 0.22 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "failure" modifying "blamed"
-3.00 1.00 NullPunisher.entity : #
-1.00 1.00 NullPunisher.other : failure
-1.00 1.00 NullPunisher.other : Bug
-3.00 1.00 NullPunisher.entity : 32-985
-0.05 1.00 NullPunisher.aux : was
-1.00 1.00 NullPunisher.other : system
-1.00 1.00 NullPunisher.other : blamed
-0.10 1.00 NullPunisher.article : the
-2.00 1.00 RootEntailment.unalignedRoot : "blamed" not aligned to anything
Hand-tuned score (dot product of above): -14.1423
Threshold: -9.4738

Inference ID: 099

Txt: Clients at the demonstration were all impressed by the system's performance. Smith was a client at the demonstration.

Hyp: Smith was impressed by the system's performance. (yes)

	Smith NNP	was VBD	impressed VBN	the DT	system NN	performance NN
Clients:NNS	1.01	0.82	0.82	0.71	0.87	0.91
the:DT	1.25	1.25	1.25	2.42	1.00	1.00
demonstration:NN	1.17	0.82	0.72	0.71	0.84	0.85
were:VBD	1.16	0.13	0.89	0.73	0.91	0.91
all:RB	1.47	0.91	0.91	1.09	1.21	1.21
impressed:VBN	1.16	0.89	1.32	0.76	0.89	0.76
the:DT	1.25	1.25	1.25	2.42	1.00	1.00
system:NN	1.05	0.82	0.80	0.71	0.28	0.96
performance:NN	1.15	0.82	0.67	0.71	0.96	0.28
Smith:NNP	0.28	1.07	1.07	0.96	1.05	1.15
was:VBD	1.16	1.32	0.89	0.76	0.91	0.91
a:DT	1.25	1.25	1.25	1.31	1.00	1.00
client:NN	1.02	0.82	0.70	0.71	0.87	0.88
the:DT	1.25	1.25	1.25	2.42	1.00	1.00
demonstration:NN	1.17	0.82	0.72	0.71	0.84	0.85
NO_WORD	0.15	1.57	0.17	0.82	0.12	0.21

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.39 Alignment.score
1.00 0.23 Alignment.isGood
-1.00 0.75 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 0.00 Alignment.hypSpan
-1.00 1.00 NullPunisher.other : impressed
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : performance
-1.00 1.00 NullPunisher.other : system
-0.05 1.00 NullPunisher.aux : was
-3.00 1.00 NullPunisher.entity : Smith
-2.00 1.00 RootEntailment.unalignedRoot : "impressed" not aligned to anything
Hand-tuned score (dot product of above): -8.8786
Threshold: -9.4738

Inference ID: 100

Txt: Clients at the demonstration were impressed by the system's performance.

Hyp: Most clients at the demonstration were impressed by the system's performance. (yes)

	Most JJS	clients NNS	the DT	demonstration NN	were VBD	impressed VBN	the DT	system NN	performance NN
Clients:NNS	0.99	1.53	0.71	0.92	0.82	0.82	0.71	0.87	0.91
the:DT	1.18	1.00	2.42	1.00	1.22	1.25	2.42	1.00	1.00
demonstration:NN	0.99	0.92	0.71	0.28	0.82	0.72	0.71	0.84	0.85
were:VBD	1.02	0.91	0.73	0.91	1.32	0.89	0.73	0.91	0.91
impressed:VBN	1.02	0.89	0.76	0.81	0.89	1.32	0.76	0.89	0.76
the:DT	1.18	1.00	2.42	1.00	1.22	1.25	2.42	1.00	1.00
system:NN	0.94	0.87	0.71	0.84	0.82	0.80	0.71	0.28	0.96
performance:NN	0.99	0.82	0.71	0.77	0.82	0.67	0.71	0.96	0.28
NO_WORD	0.11	0.15	0.82	0.20	1.57	0.17	0.82	0.12	0.21

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.57 Alignment.score
1.00 0.27 Alignment.isGood
-1.00 0.71 Alignment.isBad
-0.10 8.00 Alignment.nbWordNotAligned
0.10 1.00 Alignment.hypSpan
0.10 0.11 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "demonstration" modifying "clients"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "demonstration" modifying "Clients" is dropped on aligned hypothesis word "clients"
0.00 1.00 NegPolarity.hypNegWord : "Most": tag "JJS" is in NegPolarityMarkers list
-1.00 1.00 NullPunisher.other : Most
-1.00 1.00 NullPunisher.other : demonstration
-0.05 1.00 NullPunisher.aux : were
-1.00 1.00 NullPunisher.other : performance
-0.10 1.00 NullPunisher.article : the
-0.10 1.00 NullPunisher.article : the
-1.00 1.00 NullPunisher.other : system
-1.00 1.00 NullPunisher.other : impressed
-2.00 1.00 RootEntailment.unalignedRoot : "impressed" not aligned to anything
Hand-tuned score (dot product of above): -8.3133
Threshold: -9.4738

Inference ID: 101

Txt: University graduates make poor stock-market traders. Smith is a university graduate.

Hyp: Smith is likely to make a poor stock market trader. (yes)

	Smith NNP	is VBZ	likely JJ	to TO	make VB	a DT	poor JJ	stock-market NN	trader NN
University:NNP	1.13	0.82	0.96	0.71	0.82	0.71	0.99	0.87	0.90
graduates:NNS	1.02	0.82	0.90	0.71	0.79	0.71	0.92	0.79	0.71
make:VBP	1.14	0.84	0.78	0.76	2.10	0.76	0.88	0.83	0.86
poor:JJ	1.13	0.96	0.93	0.76	0.82	0.76	0.74	0.78	0.79
stock-market:NN	1.08	0.82	0.86	0.71	0.74	0.71	0.88	0.28	0.60
traders:NNS	1.06	0.82	0.61	0.71	0.80	0.71	0.99	0.53	1.22
Smith:NNP	0.28	1.07	1.24	0.96	1.05	0.96	1.24	1.08	1.06
is:VBZ	1.16	1.32	1.02	0.76	0.92	0.76	1.02	0.91	0.91
a:DT	1.25	1.25	1.18	1.31	1.25	2.42	1.18	1.00	1.00
university:NN	1.13	0.82	0.96	0.71	0.82	0.71	0.99	0.87	0.90
graduate:NN	1.02	0.82	0.99	0.71	0.82	0.71	0.98	0.80	0.72
NO_WORD	0.28	1.43	0.16	1.55	0.34	0.82	0.11	0.05	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.95 Alignment.score
1.00 0.35 Alignment.isGood
-1.00 0.62 Alignment.isBad
-0.10 6.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.33 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "poor" modifying "trader"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "poor" modifying "traders" is dropped on aligned hypothesis word "trader"
-0.10 1.00 NullPunisher.functionWord : to
-0.05 1.00 NullPunisher.aux : is
-0.10 1.00 NullPunisher.article : a
-1.00 1.00 NullPunisher.other : likely
-1.00 1.00 NullPunisher.other : poor
-3.00 1.00 NullPunisher.entity : Smith
-2.00 1.00 RootEntailment.unalignedRoot : "likely" not aligned to anything
Hand-tuned score (dot product of above): -7.4405
Threshold: -9.4738

Inference ID: 102

Txt: University graduates make poor stock-market traders. Smith is a university graduate.

Hyp: Smith will make a poor stock market trader. (don't know)

	Smith NNP	will MD	make VB	a DT	poor JJ	stock-market NN	trader NN
University:NNP	1.13	0.71	0.82	0.71	0.99	0.87	0.90
graduates:NNS	1.02	0.71	0.79	0.71	0.92	0.79	0.71
make:VBP	1.14	1.00	2.10	0.76	0.88	0.83	0.86
poor:JJ	1.13	0.76	0.82	0.76	0.74	0.78	0.79
stock-market:NN	1.08	0.71	0.74	0.71	0.88	0.28	0.60
traders:NNS	1.06	0.71	0.80	0.71	0.99	0.53	1.22
Smith:NNP	0.28	0.94	1.05	0.96	1.24	1.08	1.06
is:VBZ	1.16	1.11	0.92	0.76	1.02	0.91	0.91
a:DT	1.25	1.31	1.25	2.42	1.18	1.00	1.00
university:NN	1.13	0.71	0.82	0.71	0.99	0.87	0.90
graduate:NN	1.02	0.71	0.82	0.71	0.98	0.80	0.72
NO_WORD	0.28	1.55	0.17	0.82	0.11	0.05	0.09

Response: yes (INCORRECT)
Justification:
Features matched (wt val name just):
1.00 1.00 Alignment.score
1.00 0.36 Alignment.isGood
-1.00 0.61 Alignment.isBad
-0.10 4.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.43 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "poor" modifying "trader"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "poor" modifying "traders" is dropped on aligned hypothesis word "trader"
-0.10 1.00 NullPunisher.article : a
-3.00 1.00 NullPunisher.entity : Smith
-1.00 1.00 NullPunisher.other : poor
-0.05 1.00 NullPunisher.aux : will
Hand-tuned score (dot product of above): -4.0655
Threshold: -9.4738

Inference ID: 103

Txt: All APCOM managers have company cars. Jones is an APCOM manager.

Hyp: Jones has a company car. (yes)

	Jones NNP	has VBZ	a DT	company NN	car NN
All:DT	1.25	1.25	1.31	1.00	1.00
APCOM:NNP	0.95	1.07	0.96	1.07	1.07
managers:NNS	0.93	0.82	0.71	0.85	0.86
have:VBP	1.14	0.19	0.76	0.91	0.87
company:NN	1.20	0.82	0.71	0.28	0.80
cars:NNS	1.09	0.72	0.71	0.86	1.20
Jones:NNP	0.28	1.07	0.96	1.20	1.12
is:VBZ	1.16	1.01	0.76	0.91	0.91
an:DT	1.25	1.21	1.21	1.00	0.95
APCOM:NNP	0.95	1.07	0.96	1.07	1.07
manager:NN	0.95	0.82	0.71	0.96	0.86
NO_WORD	0.28	0.17	0.82	0.05	0.09

Response: yes (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.68 Alignment.score
1.00 0.29 Alignment.isGood
-1.00 0.69 Alignment.isBad
-0.10 1.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.80 Alignment.txtSpan
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "APCOM" modifying "managers" is dropped on aligned hypothesis word "Jones"
-0.10 1.00 NullPunisher.article : a
Hand-tuned score (dot product of above): 0.8655
Threshold: -9.4738

Inference ID: 104

Txt: All APCOM managers have company cars. Jones is an APCOM manager.

Hyp: Jones has more than one company car. (don't know)

	Jones NNP	has VBZ	more_than IN	one CD	company NN	car NN
All:DT	1.25	1.25	1.05	1.18	1.00	1.00
APCOM:NNP	0.95	1.07	0.75	1.20	1.07	1.07
managers:NNS	0.93	0.82	0.48	1.29	0.85	0.86
have:VBP	1.14	0.19	1.15	1.36	0.91	0.87
company:NN	1.20	0.82	0.47	1.29	0.28	0.80
cars:NNS	1.09	0.72	0.50	1.29	0.86	1.20
Jones:NNP	0.28	1.07	0.75	1.09	1.20	1.12
is:VBZ	1.16	1.01	1.15	1.39	0.91	0.91
an:DT	1.25	1.21	1.05	1.14	1.00	0.95
APCOM:NNP	0.95	1.07	0.75	1.20	1.07	1.07
manager:NN	0.95	0.82	0.50	1.29	0.96	0.86
NO_WORD	0.28	0.17	0.84	0.52	0.05	0.09

Response: dontknow (CORRECT)
Justification:
Features matched (wt val name just):
1.00 0.49 Alignment.score
1.00 0.25 Alignment.isGood
-1.00 0.73 Alignment.isBad
-0.10 2.00 Alignment.nbWordNotAligned
0.10 2.00 Alignment.hypSpan
0.10 0.67 Alignment.txtSpan
-1.00 1.00 Adjunct.addPosCxt : It is not okay that the hypothesis added the word "one" modifying "car"
0.50 1.00 Adjunct.dropPosCxt : It is okay that text word "APCOM" modifying "managers" is dropped on aligned hypothesis word "Jones"
-1.00 1.00 NullPunisher.other : more_than
-3.00 1.00 NullPunisher.entity : one
-6.00 1.00 Numeric.mismatch : NUMBER mismatch: '>1.0' vs ''
Hand-tuned score (dot product of above): -10.4225
Threshold: -9.4738