An inference model for semantic entailment in natural language: Marchine Learning Challenges

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

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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 collection1 and the PARC collection of question-answer pairs2. 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.

Original languageEnglish (US)
Title of host publicationEvaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop
PublisherSpringer-Verlag
Pages261-286
Number of pages26
ISBN (Print)3540334270, 9783540334279
StatePublished - Jan 1 2006
Event1st PASCAL Machine Learning Challenges Workshop, MLCW 2005 - Southampton, United Kingdom
Duration: Apr 11 2005Apr 13 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3944 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st PASCAL Machine Learning Challenges Workshop, MLCW 2005
CountryUnited Kingdom
CitySouthampton
Period4/11/054/13/05

Fingerprint

Knowledge representation
Knowledge Representation
Natural Language
Semantics
Predicate
Model
Resources
Optimization
Language Acquisition
Text

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

De Braz, R. S., Girju, C. R., Punyakanok, V., Roth, D., & Sammons, M. (2006). An inference model for semantic entailment in natural language: Marchine Learning Challenges. In Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop (pp. 261-286). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3944 LNAI). Springer-Verlag.

An inference model for semantic entailment in natural language : Marchine Learning Challenges. / De Braz, Rodrigo Salvo; Girju, Corina R; Punyakanok, Vasin; Roth, Dan; Sammons, Mark.

Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop. Springer-Verlag, 2006. p. 261-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3944 LNAI).

Research output: Chapter in Book/Report/Conference proceedingChapter

De Braz, RS, Girju, CR, Punyakanok, V, Roth, D & Sammons, M 2006, An inference model for semantic entailment in natural language: Marchine Learning Challenges. in Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3944 LNAI, Springer-Verlag, pp. 261-286, 1st PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, United Kingdom, 4/11/05.
De Braz RS, Girju CR, Punyakanok V, Roth D, Sammons M. An inference model for semantic entailment in natural language: Marchine Learning Challenges. In Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop. Springer-Verlag. 2006. p. 261-286. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
De Braz, Rodrigo Salvo ; Girju, Corina R ; Punyakanok, Vasin ; Roth, Dan ; Sammons, Mark. / An inference model for semantic entailment in natural language : Marchine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First PASCAL Machine Learning Challenges Workshop. Springer-Verlag, 2006. pp. 261-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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