An inference model for semantic entailment in natural language

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

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 conference › Paper › peer-review

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 language	English (US)
Pages	1043-1049
Number of pages	7
State	Published - 2005
Event	20th 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 2005 → Jul 13 2005

Other

Other	20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference, AAAI-05/IAAI-05
Country/Territory	United States
City	Pittsburgh, PA
Period	7/9/05 → 7/13/05

ASJC Scopus subject areas

Software
Artificial Intelligence

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An inference model for semantic entailment in natural language. / De Salvo Braz, Rodrigo; Girju, Corina R; Punyakanok, Vasin et al.
2005. 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.

Research output: Contribution to conference › Paper › peer-review

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AB - 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.

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Y2 - 9 July 2005 through 13 July 2005

ER -

An inference model for semantic entailment in natural language

Abstract

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