An Inference Model for Semantic Entailment in Natural Language

  • Rodrigo de Salvo Braz
  • Roxana Girju
  • Vasin Punyakanok
  • Dan Roth
  • Mark Sammons
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3944)


Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. We present a principled approach to semantic entailment that builds on inducing re-representations of text snippets into a hierarchical knowledge representation along with an optimization-based inferential mechanism that makes use of it to prove semantic entailment. This paper provides details and analysis of the knowledge representation and knowledge resources issues encountered. We analyze our system’s behavior on the PASCAL text collection and the PARC collection of question-answer pairs. This is used to motivate and explain some of the design decisions in our hierarchical knowledge representation, that is centered around a predicate-argument type abstract representation of text.


Weighting Scheme Inference Model Target Sentence Verb Processing Question Answering 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rodrigo de Salvo Braz
    • 1
  • Roxana Girju
    • 1
  • Vasin Punyakanok
    • 1
  • Dan Roth
    • 1
  • Mark Sammons
    • 1
  1. 1.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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