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COGEX at the Second RTE Marta Tatu, Brandon Iles, John Slavick, Adrian Novischi, Dan Moldovan Language Computer Corporation April 10 th, 2006

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COGEX@RTE22 LCC’s Submission to RTE2 Linear combination of three entailment scores 1.COGEX with constituency parse tree-derived logic forms 2.COGEX with dependency parse tree-derived logic forms 3.Lexical alignment between T and H For each pair i (T i,H i ) If then T i entails H i Lambda ( λ ) parameters learned on the development data for each task (IE, IR, QA, SUM)

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COGEX@RTE23 Approach to RTE with COGEX Transform the two text fragments into 3-layered logic forms Syntactic Semantic Temporal Automatically create axioms to be used during the proof Lexical Chains axioms World Knowledge axioms Linguistic transformation axioms Load COGEX’s SOS with T and H and its USABLE list of clauses with the generated axioms, Search for a proof by iteratively removing clauses from SOS and searching the USABLE for possible inferences until a refutation is found If no contradiction is detected Relax arguments Drop entire predicates from H Compute proof score semantic and temporal axioms

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COGEX@RTE24 COGEX Enhancements (1/3) Logic Form Transformation Negations not_RB(x1,e1) & walk_VB(e1,x2,x3) » - walk_VB(e1,x2,x3) not_RB(x1,e1) & walk_VB(e1,x2,x3) & fast_RB(x4,e1) » -fast_RB(x4,e1) no/DT case_NN(x1) & confirm_VB(e1,x2,x1) » - confirm_VB(e1,x2,x1)

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COGEX@RTE25 COGEX Enhancements (1/3) Logic Form Transformation Temporal normalization of date/time predicates 13 th of January 1990 vs. January 13 th, 1990 13th_of_January_1990_NN(x1) vs. January_13th_1990_NN(x1) time_TMP(BeginFN(x1), year, month, day, hour, minute, second) & time_TMP(EndFN(x1), year, month, day, hour, minute, second) time_TMP(BeginFN(x1), 1990, 1, 13, 0, 0, 0) & time_TMP(EndFN(x1), 1990, 1, 13, 23, 59, 59)

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COGEX@RTE26 COGEX Enhancements (1/3) Logic Form Transformation Temporal context SUMO predicates (Clark et al., 2005) (S,E 1,E 2 ) : S is the temporal signal linking two events E 1 and E 2 during_TMP(e1,x1), earlier_TMP(e1,x1), …

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COGEX@RTE27 Logic Forms Differences Generate LF from two different sources Constituency parse of the data Dependency parse trees (data provided by the challenge organizers) ConstituencyDependency Semantic information Temporal information Captures better the (long-range) syntactic dependencies Temporal normalization (only) NEs imported from the constituency LF whenever the tokens matched (no control over tokenization)

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COGEX@RTE28 Logic Forms Differences Gilda Flores was kidnapped on the 13 th of January 1990. Constituency: Gilda_NN(x1) & Flores_NN(x2) & nn_NNC(x3,x1,x2) & _human_NE(x3) & kidnap_VB(e1,x9,x3) & on_IN(e1,x8) & 13th_NN(x4) & of_NN(x5) & January_NN(x6) & 1990_NN(x7) & nn_ NNC(x8,x4,x5,x6,x7) & _date_NE(x8) & THM_SR(x3,e1) & TMP_SR(x8,e1) & time_TMP(BeginFN(x1), 1990, 1, 13, 0, 0, 0) & time_TMP(EndFN(x1), 1990, 1, 13, 23, 59, 59) & during_TMP(e1,x8) Dependency: Gilda_Flores_NN(x2) & _human_NE(x2) & kidnap_VB(e1,x4,x2) & on_IN(e1,x3) & 13th_NN(x3) & of_IN(x3,x1) & January_1990_NN(x1)

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COGEX@RTE29 COGEX Enhancements (2/3) Axioms on Demand Lexical Chains Consider the first k =3 senses for each word Maximum length of a lexical chain = 3 DERIVATIONAL WordNet relation is ambiguous with respect to the role of the noun Derivation-ACT: employ_VB(e1,x1,x2) → employment_NN(e1) Derivation-AGENT: employ_VB(e1,x1,x2) → employer_NN(x1) Derivation-THEME: employ_VB(e1,x1,x2) → employee_NN(x2) Morphological derivations between adjectives and verbs

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COGEX@RTE210 COGEX Enhancements (2/3) Axioms on Demand Lexical Chains Augment with the NE predicate for NE target concepts nicaraguan_JJ(x1,x2) → Nicaragua_NN(x1) & _country_NE(x1) Discard lexical chains with more than 2 HYPONYMY relations (H too specific) with a HYPONYMY followed by an ISA Chicago_NN(x1) → Detroit_NN(x1) which include general concepts: object/NN, act/VB, be/VB n i = number of hyponyms of concept c i N = number of concepts in c i ’s hierarchy

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COGEX@RTE211 More Axioms Another 73 World Knowledge axioms Semantic Calculus – combinations of two semantic relations (82 axioms) ISA, KINSHIP, CAUSE are transitive relations ISA_SR(x1,x2) & PAH_SR(x3,x2) → PAH_SR(x3,x2) Mike is a rich man → Mike is rich Temporal Reasoning Axioms (Clark et al., 2005) (65 axioms) Dates entail more general times October 2000 → year 2000 during_TMP(e1,e2) & during_TMP(e2,e3) → during_TMP(e1,e3)

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COGEX@RTE212 COGEX Enhancements (3/3) Proof Re-Scoring (T) smart people → people (H) (T) people → smart people (H) Entities mentioned in T and H are existentially quantified Universally quantified T and H entities (T) people → smart people (H) (T) smart people → people (H)

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COGEX@RTE213 Shallow Lexical Alignment Compute the edit distance between T and H Cost (deletion of a word from T) = 0 Cost (replace of a word from T with another in H) = ∞ Cost (insert a word from H) = Edit distance between synonyms = 0 T:The Council of Europehas45 member states.Three countries from … DELINSDEL H:The Council of Europeis made up by45 member states.

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COGEX@RTE214 Results Learned parameters: IE: score given by COGEX C with some correction from COGEX D IR: the highest contribution is made by LexAlign (~62%) COGEX D better on IE, IR, QA (~69% accuracy) COGEX C better on SUM (~66% accuracy) Three-way combination outperforms any individual results and any two-system combination

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COGEX@RTE215 Results, Future Work Higher accuracy on the SUM task SUM is the highest accuracy task for all systems (false entailment pairs had H completely unrelated with the texts T) IE: highest number of false positives Future enhancements Other types of context: report, planning, etc. Need for more axioms Automatic gathering of semantic axioms Paraphrase acquisition (phrase 1 → phrase 2 )

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Thank You ! Questions?

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