A machine learning approach to textual entailment recognition

Fabio Massimo Zanzotto, Marco Pennacchiotti, Alessandro Moschitti

Research output: Contribution to journalArticle

48 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)551-582
Number of pages32
JournalNatural Language Engineering
Issue number4
Publication statusPublished - 1 Oct 2009


ASJC Scopus subject areas

  • Software
  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

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