An inference model for semantic entailment in natural language

Rodrigo De Salvo Braz, Corina R Girju, Vasin Punyakanok, Dan Roth, Mark Sammons

Research output: Contribution to conferencePaper

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. This paper presents a principled approach to this problem that builds on inducing representations of text snippets into a hierarchical knowledge representation along with a sound optimization-based inferential mechanism that makes use of it to decide semantic entailment. A preliminary evaluation on the PASCAL text collection is presented.

Original languageEnglish (US)
Pages1043-1049
Number of pages7
StatePublished - Dec 1 2005
Event20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05 - Pittsburgh, PA, United States
Duration: Jul 9 2005Jul 13 2005

Other

Other20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05
CountryUnited States
CityPittsburgh, PA
Period7/9/057/13/05

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    De Salvo Braz, R., Girju, C. R., Punyakanok, V., Roth, D., & Sammons, M. (2005). An inference model for semantic entailment in natural language. 1043-1049. Paper presented at 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05, Pittsburgh, PA, United States.