Learning Parse-Free Event-Based Features for Textual Entailment Recognition

  • Bahadorreza Ofoghi
  • John Yearwood
Conference paper

DOI: 10.1007/978-3-642-17432-2_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6464)
Cite this paper as:
Ofoghi B., Yearwood J. (2010) Learning Parse-Free Event-Based Features for Textual Entailment Recognition. In: Li J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science, vol 6464. Springer, Berlin, Heidelberg

Abstract

We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these features can improve the effectiveness of the identification of entailment and no-entailment relationships.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bahadorreza Ofoghi
    • 1
    • 2
  • John Yearwood
    • 1
  1. 1.Centre for Informatics and Applied OptimizationUniversity of BallaratBallaratAustralia
  2. 2.Institute of Sport, Exercise, and Active LivingVictoria UniversityMelbourneAustralia

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