COGEX at the Second RTE

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COGEX at the Second RTE

• Marta Tatu, Brandon Iles, John Slavick, Adrian
  Novischi, Dan Moldovan
• Language Computer Corporation
• April 10th, 2006

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LCCs 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 (?) parameters learned on the development
  data for each task (IE, IR, QA, SUM)

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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 COGEXs 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 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 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)

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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),

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Logic Forms Differences

• Generate LF from two different sources
• Constituency parse of the data
• Dependency parse trees (data provided by the
  challenge organizers)

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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)

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COGEX Enhancements (2/3)

• Axioms on Demand
• Lexical Chains
• Consider the first k3 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 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 cis hierarchy

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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 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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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)
  8
• Cost (insert a word from H)
• Edit distance between synonyms 0

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Results
Learned parameters

• 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

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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 (phrase1 ? phrase2)

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Thank You !

• Questions?