RTE5 - Ablation Tests

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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
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 relations are used to calculate relatedness between verbs in T and H
VerbOcean DFKI2.3way 0.33 0.5 VerbOcean relations are used to calculate relatedness between verbs in T and H
VerbOcean DFKI3.3way 0.17 0.17 VerbOcean relations are used to calculate relatedness between verbs in T and H
VerbOcean FBKirst1.2way -0.16 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 VerbOcean is used to calculate relatedness between nominal predicates in T and H, after using WordNet to change the nouns into verbs.
WordNet +
VerbOcean
DFKI2.3way 0.5 0.67 VerbOcean is used to calculate relatedness between nominal predicates in T and H, after using WordNet to change the nouns into verbs.
WordNet +
VerbOcean
DFKI3.3way 0.17 0.17 VerbOcean is used to calculate relatedness between nominal predicates in T and H, after using WordNet to change the nouns into verbs.
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 participant themselves)
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 participant themselves)
WordNet +
XWordNet
UAIC20091.3way 1 1.33 Synonymy, hyponymy and hypernymy and eXtended WordNet relation
System component DirRelCond3.2way 4.67 The ablation test (abl-1) was meant to test one component of the most complex condition for entailment used in step 3 of the system
System component DirRelCond3.2way -1.5 The ablation test (abl-2) was meant to test one component of the most complex condition for entailment used in step 3 of the system
System component DirRelCond3.2way 0.17 The ablation test (abl-3) was meant to test one component of the most complex condition for entailment used in step 3 of the system
System component DirRelCond3.2way -1.16 The ablation test (abl-4) was meant to test one component of the most complex condition for entailment used in step 3 of the system
System component DirRelCond3.2way 4.17 The ablation test (abl-5) was meant to test one component of the most complex condition for entailment used in step 3 of the system
System component UAIC20091.3way 4.17 4 Pre-processing module
Other DLSIUAES1.2way 1 Everything ablated except lexical-based metrics
Other DLSIUAES1.2way 3.33 Everything ablated except semantic-derived inferences
Other DLSIUAES1.3way -0.17 -0.33 Everything ablated except lexical-based metrics
Other DLSIUAES1.3way 2.33 3.17 Everything ablated except semantic-derived inferences
Other FBKirst1.2way 2.84 The automatic estimation of operation costs from run-1 modules was removed: the set of costs were assigned manually.
Other JU_CSE_TAC1.2way 0 Named Entity match
Other JU_CSE_TAC1.2way 0 Skip bigram
Other JU_CSE_TAC1.2way 0 Bigram match
Other JU_CSE_TAC1.2way -0.5 Longest Common Subsequence
Other JU_CSE_TAC1.2way -0.5 Unigram match after stemming
Other PeMoZa1.2way -2.5 Idf score
Other PeMoZa1.2way -0.66 Proper Noun Levenstain Distance
Other PeMoZa1.2way 0.34 J&C (Jiang and Conrath, 1997) similarity score on nouns, adjectives
Other PeMoZa2.2way 1 Idf score
Other PeMoZa2.2way 0.17 Proper Noun Levenstain Distance
Other PeMoZa2.2way 0.5 J&C (Jiang and Conrath, 1997) similarity score on nouns, adjectives
Other UI_ccg1.2way 1 Less sophisticated NE similarity metric: mainly Jaro-Winkler-based