Automatic learning of textual entailments with cross-pair similarities

Fabio Massimo Zanzotto, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Citations (Scopus)

Abstract

In this paper we define a novel similarity measure between examples of textual entailments and we use it as a kernel function in Support Vector Machines (SVMs). This allows us to automatically learn the rewrite rules that describe a non trivial set of entailment cases. The experiments with the data sets of the RTE 2005 challenge show an improvement of 4.4% over the state-of-the-art methods.

Original languageEnglish
Title of host publicationCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Pages401-408
Number of pages8
Publication statusPublished - 1 Dec 2006
Event21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW, Australia
Duration: 17 Jul 200621 Jul 2006

Publication series

NameCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume1

Other

Other21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
CountryAustralia
CitySydney, NSW
Period17/7/0621/7/06

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Zanzotto, F. M., & Moschitti, A. (2006). Automatic learning of textual entailments with cross-pair similarities. In COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 401-408). (COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference; Vol. 1).