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