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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. LCC’s Submission to RTE2. Linear combination of three entailment scores COGEX with constituency parse tree-derived logic forms

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cogex at the second rte

COGEX at the Second RTE

Marta Tatu, Brandon Iles, John Slavick, Adrian Novischi, Dan Moldovan

Language Computer Corporation

April 10th, 2006

lcc s submission to rte2
LCC’s Submission to RTE2
  • Linear combination of three entailment scores
      • COGEX with constituency parse tree-derived logic forms
      • COGEX with dependency parse tree-derived logic forms
      • Lexical alignment between T and H

For each pair i (Ti,Hi)

If

then Ti entails Hi

    • Lambda(λ) parameters learned on the development data for each task (IE, IR, QA, SUM)

@2006 Language Computer Corporation

semantic based logic approach
Semantic-based Logic Approach
  • Textual Entailment
    • Task definition: T entails H, denoted by T → H, if the meaning of H can be inferred from the meaning of T
    • inferred » logic (theorem prover + axioms)
    • meaning » semantics (semantic-enhanced representation)

@2006 Language Computer Corporation

approach to rte with cogex
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

@2006 Language Computer Corporation

cogex enhancements 1 3
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)

@2006 Language Computer Corporation

cogex enhancements 1 31
COGEX Enhancements (1/3)
  • Logic Form Transformation
    • Temporal normalization of date/time predicates
      • 13th of January 1990 vs. January 13th, 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)

@2006 Language Computer Corporation

cogex enhancements 1 32
COGEX Enhancements (1/3)
  • Logic Form Transformation
    • Temporal context SUMO predicates (Clark et al., 2005)
      • (S,E1,E2) : S is the temporal signal linking two events E1 and E2
      • during_TMP(e1,x1), earlier_TMP(e1,x1), …

@2006 Language Computer Corporation

logic forms differences
Logic Forms Differences
  • Generate LF from two different sources
    • Constituency parse of the data
    • Dependency parse trees (data provided by the challenge organizers)

@2006 Language Computer Corporation

logic forms differences1
Logic Forms Differences
  • Gilda Flores was kidnapped on the 13th 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)

@2006 Language Computer Corporation

cogex enhancements 2 3
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

@2006 Language Computer Corporation

cogex enhancements 2 31
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
          • ni= number of hyponyms of concept ci
          • N = number of concepts in ci’s hierarchy

@2006 Language Computer Corporation

more axioms
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)

@2006 Language Computer Corporation

cogex enhancements 3 3
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)

@2006 Language Computer Corporation

shallow lexical alignment
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

@2006 Language Computer Corporation

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

Learned parameters:

@2006 Language Computer Corporation

results1
Results
  • 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
  • Need more World Knowledge
    • (QA task) 15 safety violations → numerous safety violations
  • Upper bound (human performance) for RTE2 test
    • 97% proportional agreement
    • Kappa agreement: K = 0.94 (good agreement)
    • Fewer controversial examples in this year’s test
  • Performance on RTE1 test: 69% accuracy

@2006 Language Computer Corporation

future work
Future Work
  • Other types of context: report, planning, etc.
    • Pairs (T:X said Y, H:Y) labeled as both TRUE and FALSE
  • Need for more axioms
    • Paraphrase acquisition (phrase1→ phrase2)
    • Automatic gathering of semantic axioms
      • Lexical chains link only concepts
      • WordNet gloss axioms link a concept to a phrase

@2006 Language Computer Corporation

thank you

Thank You !

Questions?

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