RTE5 - Ablation Tests

Ablated Resource	Team Run	Relative accuracy - 2way	Relative 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