An inference model for semantic entailment in natural language

Authors:
Rodrigo De Salvo Braz;Roxana Girju;Vasin Punyakanok;Dan Roth;Mark Sammons
Affiliations:
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. We present a principled approach to this problem that builds on inducing re-representations of text snippets into a hierarchical knowledge representation along with a sound inferential mechanism that makes use of it to prove semantic entailment.