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COGEX at the Second RTE

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Linear combination of three entailment scores. COGEX with constituency parse tree-derived logic forms ... Gilda Flores was kidnapped on the 13th of January 1990. ... – PowerPoint PPT presentation

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Title: COGEX at the Second RTE


1
COGEX at the Second RTE
  • Marta Tatu, Brandon Iles, John Slavick, Adrian
    Novischi, Dan Moldovan
  • Language Computer Corporation
  • April 10th, 2006

2
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)

3
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
4
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)

5
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)

6
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),

7
Logic Forms Differences
  • Generate LF from two different sources
  • Constituency parse of the data
  • Dependency parse trees (data provided by the
    challenge organizers)

8
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)

9
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

10
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

11
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)

12
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)

13
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

14
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

15
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)

16
Thank You !
  • Questions?
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