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Two Related Lexico-Syntactic Approaches to Entailment. Vasile Rus Institute for Intelligent Systems Department of Computer Science http://www.cs.memphis.edu/~vrus. TODAY- Outline. General strategy Map T and H into lexico-syntactic graphs

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two related lexico syntactic approaches to entailment

Two Related Lexico-Syntactic Approaches to Entailment

Vasile Rus

Institute for Intelligent Systems

Department of Computer Science

http://www.cs.memphis.edu/~vrus

today outline
TODAY- Outline
  • General strategy
    • Map T and H into lexico-syntactic graphs
    • Perform graph subsumption between graph-T and graph-H
    • Additive strategy
      • Not cascaded
  • Two approaches
    • Lexico-syntactic approach
    • Dependency-based approach
  • Results, Comparison, Conclusions
the two approaches
The Two Approaches
  • Lexico-syntactic approach
    • Lexical component
    • Syntactic component
    • Dependencies derived from phrase-based parse trees
    • Negation
    • thesaurus
  • Dependency-based approach
    • Dependencies from MINIPAR
    • Lexical component by default
    • Postprocessing (thanks to Vivi Nastase)
      • To eliminate unused information
      • To retain only dependencies among content words
graph subsumption
Graph Subsumption
  • Map nodes and edges in H-graph to nodes and edges in T-graph
  • complex mapping based on
    • Named Entity Inferences: Overture Services Inc -- Overture
    • Word-level entailment / equivalence: take over – buy
    • Syntactic Info:
      • Yahoo is the agent of buying
from sentences to graph representation
From Sentences to Graph Representation
  • vertices represent content words
  • edges represent dependencies
    • local dependencies (intra-phrase) are straightforwardly obtained from a parse tree
    • remote dependencies are obtained using an extended functional tagger
    • Or from MINIPAR (for the second approach)
the entailment score
The Entailment Score
  • The score is so defined to be non-reflexive:
    • entail(T, H) ≠ entail(H,T)

Score is also used as confidence

the parameters
The Parameters
  • the following parameters worked best on development

α=.5 β =.5 γ=0

negation
Negation
  • Explicit
    • Clue phrases
      • no, not, neither … nor
      • shortened forms: ‘nt
  • Implicit
    • Antonymy in WordNet
  • Hypothetical sentences:

“a possible visit by Clinton to China”

does not entail

“Clinton visited China”

    • a form of negation
conclusions
Conclusions
  • Lexical information significantly helps
  • The other components (synonymy, dependencies, negation) add value but not significantly
missed opportunities
Missed Opportunities
  • Linguistic Level
    • Five = 5
    • Tuscany province = province of Tuscany
  • Current subsumption algorithm is weak
      • T: Besancon is the capital of France’s watch and clock-making industry and of high precision engineering.
      • H: Besancon is the capital of France.

Solution: matching with more complex structures

  • World Knowledge
more conclusions
More Conclusions
  • Our system is light
    • Good for interactive environment such as Intelligent Tutoring Systems
  • No training involved
    • Just development to tune few parameters
one more conclusion
One More Conclusion
  • It is not clear whether there is a difference among the two ways to obtain dependencies!