Difference between revisions of "RTE5 - Ablation Tests"

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! Ablated Resource
 
! Ablated Resource
 
! Team Run
 
! Team Run
! <small>Relative accuracy - 2way</small>
+
! <small>&Delta; Accuracy % - 2way</small>
! <small>Relative accuracy - 3way</small>
+
! <small>&Delta; Accuracy % - 3way</small>
 
! Resource Usage Description
 
! Resource Usage Description
  
Line 18: Line 18:
 
| Acronym guide + <br>UAIC_Acronym_rules  
 
| Acronym guide + <br>UAIC_Acronym_rules  
 
| UAIC20091.3way
 
| UAIC20091.3way
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
| style="text-align: center;"| +0.16
+
| style="text-align: center;"| 0.16
 
| We start from acronym-guide, but additional we use a rule that consider for expressions like Xaaaa Ybbbb Zcccc the acronym XYZ, regardless of length of text with this form.
 
| We start from acronym-guide, but additional we use a rule that consider for expressions like Xaaaa Ybbbb Zcccc the acronym XYZ, regardless of length of text with this form.
  
Line 25: Line 25:
 
| DIRT
 
| DIRT
 
| BIU1.2way
 
| BIU1.2way
| style="text-align: center;"| +1.33
+
| style="text-align: center;"| 1.33
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Inference rules
 
| Inference rules
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| DIRT
 
| DIRT
 
| UAIC20091.3way
 
| UAIC20091.3way
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
| style="text-align: center;"| +0.33
+
| style="text-align: center;"| 0.33
 
| We transform text and hypothesis with MINIPAR into dependency trees: use of DIRT relations to map verbs in T with verbs in H
 
| We transform text and hypothesis with MINIPAR into dependency trees: use of DIRT relations to map verbs in T with verbs in H
  
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| Framenet
 
| Framenet
 
| DLSIUAES1.2way
 
| DLSIUAES1.2way
| style="text-align: center;"| +1.16
+
| style="text-align: center;"| 1.16
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| frame-to-frame similarity metric
 
| frame-to-frame similarity metric
Line 67: Line 67:
 
| Grady Ward’s MOBY Thesaurus + <br>Roget's Thesaurus
 
| Grady Ward’s MOBY Thesaurus + <br>Roget's Thesaurus
 
| VensesTeam2.2way
 
| VensesTeam2.2way
| style="text-align: center;"| +2.83
+
| style="text-align: center;"| 2.83
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Semantic fields are used as semantic similarity matching, in all cases of non identical lemmas
 
| Semantic fields are used as semantic similarity matching, in all cases of non identical lemmas
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| NER
 
| NER
 
| UI_ccg1.2way
 
| UI_ccg1.2way
| style="text-align: center;"| +4.83
+
| style="text-align: center;"| 4.83
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Named Entity recognition/comparison
 
| Named Entity recognition/comparison
Line 95: Line 95:
 
| PropBank
 
| PropBank
 
| cswhu1.3way
 
| cswhu1.3way
| style="text-align: center;"| +2
+
| style="text-align: center;"| 2
| style="text-align: center;"| +3.17
+
| style="text-align: center;"| 3.17
 
| syntactic and semantic parsing
 
| syntactic and semantic parsing
  
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| Stanford NER
 
| Stanford NER
 
| QUANTA1.2way
 
| QUANTA1.2way
| style="text-align: center;"| +0.67
+
| style="text-align: center;"| 0.67
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| We use Named Entity similarity as a feature
 
| We use Named Entity similarity as a feature
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| Stopword list
 
| Stopword list
 
| FBKirst1.2way
 
| FBKirst1.2way
| style="text-align: center;"| +1.5
+
| style="text-align: center;"| 1.5
 
| style="text-align: center;"| -10.28
 
| style="text-align: center;"| -10.28
 
|  
 
|  
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| Training data from RTE2
 
| Training data from RTE2
 
| PeMoZa3.2way
 
| PeMoZa3.2way
| style="text-align: center;"| +0.66
+
| style="text-align: center;"| 0.66
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
|  
 
|  
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| DFKI1.3way
 
| DFKI1.3way
 
| style="text-align: center;"| 0
 
| style="text-align: center;"| 0
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
 
|  
 
|  
  
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| VerbOcean
 
| VerbOcean
 
| DFKI2.3way
 
| DFKI2.3way
| style="text-align: center;"| +0.33
+
| style="text-align: center;"| 0.33
| style="text-align: center;"| +0.5
+
| style="text-align: center;"| 0.5
 
|  
 
|  
  
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| VerbOcean
 
| VerbOcean
 
| DFKI3.3way
 
| DFKI3.3way
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
 
|  
 
|  
  
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| WikiPedia
 
| WikiPedia
 
| cswhu1.3way
 
| cswhu1.3way
| style="text-align: center;"| +1.33
+
| style="text-align: center;"| 1.33
| style="text-align: center;"| +3.34
+
| style="text-align: center;"| 3.34
 
| Lexical semantic rules
 
| Lexical semantic rules
  
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| WikiPedia
 
| WikiPedia
 
| FBKirst1.2way
 
| FBKirst1.2way
| style="text-align: center;"| +1
+
| style="text-align: center;"| 1
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Rules extracted from WP using Latent Semantic Analysis (LSA)
 
| Rules extracted from WP using Latent Semantic Analysis (LSA)
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| WikiPedia
 
| WikiPedia
 
| UAIC20091.3way
 
| UAIC20091.3way
| style="text-align: center;"| +1.17
+
| style="text-align: center;"| 1.17
| style="text-align: center;"| +1.5
+
| style="text-align: center;"| 1.5
 
| Relations between named entities
 
| Relations between named entities
  
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| Wikipedia + <br>NER's (LingPipe, GATE) + <br>Perl patterns
 
| Wikipedia + <br>NER's (LingPipe, GATE) + <br>Perl patterns
 
| UAIC20091.3way
 
| UAIC20091.3way
| style="text-align: center;"| +6.17
+
| style="text-align: center;"| 6.17
| style="text-align: center;"| +5
+
| style="text-align: center;"| 5
 
| NE module: NERs, in order to identify Persons, Locations, Jobs, Languages, etc; Perl patterns built by us for RTE4 in order to identify numbers and dates; our own resources extracted from Wikipedia in order to identify a "distance" between one name entity from hypothesis and name entities from text
 
| NE module: NERs, in order to identify Persons, Locations, Jobs, Languages, etc; Perl patterns built by us for RTE4 in order to identify numbers and dates; our own resources extracted from Wikipedia in order to identify a "distance" between one name entity from hypothesis and name entities from text
  
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| WordNet
 
| WordNet
 
| BIU1.2way
 
| BIU1.2way
| style="text-align: center;"| +2.5
+
| style="text-align: center;"| 2.5
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Synonyms, hyponyms (2 levels away from the original term), hyponym_instance and derivations
 
| Synonyms, hyponyms (2 levels away from the original term), hyponym_instance and derivations
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| WordNet
 
| WordNet
 
| Boeing3.3way
 
| Boeing3.3way
| style="text-align: center;"| +4  
+
| style="text-align: center;"| 4  
| style="text-align: center;"| +5.67
+
| style="text-align: center;"| 5.67
 
|  
 
|  
  
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| WordNet
 
| WordNet
 
| DFKI2.3way
 
| DFKI2.3way
| style="text-align: center;"| +0.16
+
| style="text-align: center;"| 0.16
| style="text-align: center;"| +0.34
+
| style="text-align: center;"| 0.34
 
|  
 
|  
  
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| WordNet
 
| WordNet
 
| DFKI3.3way
 
| DFKI3.3way
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
 
|  
 
|  
  
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| WordNet
 
| WordNet
 
| DLSIUAES1.2way
 
| DLSIUAES1.2way
| style="text-align: center;"| +0.83
+
| style="text-align: center;"| 0.83
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Similarity between lemmata, computed by WordNet-based metrics
 
| Similarity between lemmata, computed by WordNet-based metrics
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| DLSIUAES1.3way
 
| DLSIUAES1.3way
 
| style="text-align: center;"| -0.5
 
| style="text-align: center;"| -0.5
| style="text-align: center;"| -0.33
+
| style="text-align: center;"| &minus;0.33
 
| Similarity between lemmata, computed by WordNet-based metrics
 
| Similarity between lemmata, computed by WordNet-based metrics
  
Line 277: Line 277:
 
| WordNet
 
| WordNet
 
| JU_CSE_TAC1.2way
 
| JU_CSE_TAC1.2way
| style="text-align: center;"| +0.34
+
| style="text-align: center;"| 0.34
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| WordNet based Unigram match
 
| WordNet based Unigram match
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| WordNet
 
| WordNet
 
| PeMoZa1.2way
 
| PeMoZa1.2way
| style="text-align: center;"| +1.33
+
| style="text-align: center;"| 1.33
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Verb Entailment from Wordnet
 
| Verb Entailment from Wordnet
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| WordNet
 
| WordNet
 
| PeMoZa2.2way
 
| PeMoZa2.2way
| style="text-align: center;"| +1
+
| style="text-align: center;"| 1
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Derivational Morphology from WordNet
 
| Derivational Morphology from WordNet
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| WordNet
 
| WordNet
 
| Siel_093.3way
 
| Siel_093.3way
| style="text-align: center;"| +0.34
+
| style="text-align: center;"| 0.34
 
| style="text-align: center;"| -0.17
 
| style="text-align: center;"| -0.17
 
| Similarity between nouns using WN tool
 
| Similarity between nouns using WN tool
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| ssl1.3way
 
| ssl1.3way
 
| style="text-align: center;"| 0
 
| style="text-align: center;"| 0
| style="text-align: center;"| +0.67
+
| style="text-align: center;"| 0.67
 
| WordNet Analysis
 
| WordNet Analysis
  
Line 348: Line 348:
 
| WordNet
 
| WordNet
 
| UI_ccg1.2way
 
| UI_ccg1.2way
| style="text-align: center;"| +4  
+
| style="text-align: center;"| 4  
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| word similarity == identity
 
| word similarity == identity
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| DFKI1.3way
 
| DFKI1.3way
 
| style="text-align: center;"| 0
 
| style="text-align: center;"| 0
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
 
|  
 
|  
  
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| WordNet +<br>VerbOcean
 
| WordNet +<br>VerbOcean
 
| DFKI2.3way
 
| DFKI2.3way
| style="text-align: center;"| +0.5
+
| style="text-align: center;"| 0.5
| style="text-align: center;"| +0.67
+
| style="text-align: center;"| 0.67
 
|  
 
|  
  
Line 376: Line 376:
 
| WordNet +<br>VerbOcean
 
| WordNet +<br>VerbOcean
 
| DFKI3.3way
 
| DFKI3.3way
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
| style="text-align: center;"| +0.17
+
| style="text-align: center;"| 0.17
 
|  
 
|  
  
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| WordNet +<br>VerbOcean
 
| WordNet +<br>VerbOcean
 
| UAIC20091.3way
 
| UAIC20091.3way
| style="text-align: center;"| +2
+
| style="text-align: center;"| 2
| style="text-align: center;"| +1.50
+
| style="text-align: center;"| 1.50
 
| Contradiction identification
 
| Contradiction identification
  
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| WordNet +<br>VerbOcean + <br>DLSIUAES_negation_list
 
| WordNet +<br>VerbOcean + <br>DLSIUAES_negation_list
 
| DLSIUAES1.2way
 
| DLSIUAES1.2way
| style="text-align: center;"| +0.66
+
| style="text-align: center;"| 0.66
 
| style="text-align: center;"|  
 
| style="text-align: center;"|  
 
| Antonym relations between verbs (VO+WN); polarity based on negation terms (short list constructed by ourselves)
 
| Antonym relations between verbs (VO+WN); polarity based on negation terms (short list constructed by ourselves)
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| WordNet +<br>XWordNet
 
| WordNet +<br>XWordNet
 
| UAIC20091.3way
 
| UAIC20091.3way
| style="text-align: center;"| +1
+
| style="text-align: center;"| 1
| style="text-align: center;"| +1.33
+
| style="text-align: center;"| 1.33
 
| Synonymy, hyponymy and hypernymy and eXtended WordNet relation
 
| Synonymy, hyponymy and hypernymy and eXtended WordNet relation
  
 
|}
 
|}

Revision as of 08:21, 30 November 2009

Ablated Resource Team Run Δ Accuracy % - 2way Δ Accuracy % - 3way Resource Usage Description
Acronym guide Siel_093.3way 0 0 Acronym Resolution
Acronym guide +
UAIC_Acronym_rules
UAIC20091.3way 0.17 0.16 We start from acronym-guide, but additional we use a rule that consider for expressions like Xaaaa Ybbbb Zcccc the acronym XYZ, regardless of length of text with this form.
DIRT BIU1.2way 1.33 Inference rules
DIRT Boeing3.3way -1.17 0
DIRT UAIC20091.3way 0.17 0.33 We transform text and hypothesis with MINIPAR into dependency trees: use of DIRT relations to map verbs in T with verbs in H
Framenet DLSIUAES1.2way 1.16 frame-to-frame similarity metric
Framenet DLSIUAES1.3way -0.17 -0.17 frame-to-frame similarity metric
Framenet UB.dmirg3.2way 0
Grady Ward’s MOBY Thesaurus +
Roget's Thesaurus
VensesTeam2.2way 2.83 Semantic fields are used as semantic similarity matching, in all cases of non identical lemmas
MontyLingua Tool Siel_093.3way 0 0 For the VerbOcean, the verbs have to be in the base form. We used the "MontyLingua" tool to convert the verbs into their base form
NEGATION_rules by UAIC UAIC20091.3way 0 -1.34 Negation rules check in the dependency trees on verbs descending branches to see if some categories of words that change the meaning are found.
NER UI_ccg1.2way 4.83 Named Entity recognition/comparison
PropBank cswhu1.3way 2 3.17 syntactic and semantic parsing
Stanford NER QUANTA1.2way 0.67 We use Named Entity similarity as a feature
Stopword list FBKirst1.2way 1.5 -10.28
Training data from RTE1, 2, 3 PeMoZa3.2way 0
Training data from RTE1, 2, 3 PeMoZa3.2way 0
Training data from RTE2 PeMoZa3.2way 0.66
Training data from RTE2, 3 PeMoZa3.2way 0
VerbOcean DFKI1.3way 0 0.17
VerbOcean DFKI2.3way 0.33 0.5
VerbOcean DFKI3.3way 0.17 0.17
VerbOcean FBKirst1.2way -0.16 -10.28 Rules extracted from VerbOcean
VerbOcean QUANTA1.2way 0 We use "opposite-of" relation in VerbOcean as a feature
VerbOcean Siel_093.3way 0 0 Similarity/anthonymy/unrelatedness between verbs
WikiPedia BIU1.2way -1 Lexical rules extracted from Wikipedia definition sentences, title parenthesis, redirect and hyperlink relations
WikiPedia cswhu1.3way 1.33 3.34 Lexical semantic rules
WikiPedia FBKirst1.2way 1 Rules extracted from WP using Latent Semantic Analysis (LSA)
WikiPedia UAIC20091.3way 1.17 1.5 Relations between named entities
Wikipedia +
NER's (LingPipe, GATE) +
Perl patterns
UAIC20091.3way 6.17 5 NE module: NERs, in order to identify Persons, Locations, Jobs, Languages, etc; Perl patterns built by us for RTE4 in order to identify numbers and dates; our own resources extracted from Wikipedia in order to identify a "distance" between one name entity from hypothesis and name entities from text
WordNet AUEBNLP1.3way -2 -2.67 Synonyms
WordNet BIU1.2way 2.5 Synonyms, hyponyms (2 levels away from the original term), hyponym_instance and derivations
WordNet Boeing3.3way 4 5.67
WordNet DFKI1.3way -0.17 0
WordNet DFKI2.3way 0.16 0.34
WordNet DFKI3.3way 0.17 0.17
WordNet DLSIUAES1.2way 0.83 Similarity between lemmata, computed by WordNet-based metrics
WordNet DLSIUAES1.3way -0.5 −0.33 Similarity between lemmata, computed by WordNet-based metrics
WordNet JU_CSE_TAC1.2way 0.34 WordNet based Unigram match
WordNet PeMoZa1.2way -0.5 Derivational Morphology from WordNet
WordNet PeMoZa1.2way 1.33 Verb Entailment from Wordnet
WordNet PeMoZa2.2way 1 Derivational Morphology from WordNet
WordNet PeMoZa2.2way -0.33 Verb Entailment from Wordnet
WordNet QUANTA1.2way -0.17 We use several relations from wordnet, such as synonyms, hyponym, hypernym et al.
WordNet Sagan1.3way 0 -0.83 The system is based on machine learning approach. The ablation test was obtained with 2 less features using WordNet in the training and testing steps.


WordNet Siel_093.3way 0.34 -0.17 Similarity between nouns using WN tool
WordNet ssl1.3way 0 0.67 WordNet Analysis
WordNet UB.dmirg3.2way 0
WordNet UI_ccg1.2way 4 word similarity == identity
WordNet +
FrameNet
UB.dmirg3.2way 0
WordNet +
VerbOcean
DFKI1.3way 0 0.17
WordNet +
VerbOcean
DFKI2.3way 0.5 0.67
WordNet +
VerbOcean
DFKI3.3way 0.17 0.17
WordNet +
VerbOcean
UAIC20091.3way 2 1.50 Contradiction identification
WordNet +
VerbOcean +
DLSIUAES_negation_list
DLSIUAES1.2way 0.66 Antonym relations between verbs (VO+WN); polarity based on negation terms (short list constructed by ourselves)
WordNet +
VerbOcean +
DLSIUAES_negation_list
DLSIUAES1.3way -1 -0.5 Antonym relations between verbs (VO+WN); polarity based on negation terms (short list constructed by ourselves)
WordNet +
XWordNet
UAIC20091.3way 1 1.33 Synonymy, hyponymy and hypernymy and eXtended WordNet relation