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A machine learning approach to textual entailment recognition

Published online by Cambridge University Press:  16 September 2009

FABIO MASSIMO ZANZOTTO ,
MARCO PENNACCHIOTTI  and
ALESSANDRO MOSCHITTI
FABIO MASSIMO ZANZOTTO
Affiliation:
DISP, University of Rome ‘Tor Vergata’, Roma, Italy e-mail: [email protected]
MARCO PENNACCHIOTTI
Affiliation:
Computerlinguistik, Universität des Saarlandes, Saarbrücken, Germany e-mail: [email protected]
ALESSANDRO MOSCHITTI
Affiliation:
DISI, University of Trento, Povo di Trento, Italy e-mail: [email protected]
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Abstract

Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so.

Type
Papers
Information
Natural Language Engineering , Volume 15 , Special Issue 4: Textual Entailment , October 2009 , pp. 551 - 582
Copyright
Copyright © Cambridge University Press 2009

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