Recognizing Textual Entailment with Statistical Methods

  • Miguel Angel Ríos Gaona
  • Alexander Gelbukh
  • Sivaji Bandyopadhyay
Conference paper

DOI: 10.1007/978-3-642-15992-3_39

Volume 6256 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Ríos Gaona M.A., Gelbukh A., Bandyopadhyay S. (2010) Recognizing Textual Entailment with Statistical Methods. In: Martínez-Trinidad J.F., Carrasco-Ochoa J.A., Kittler J. (eds) Advances in Pattern Recognition. MCPR 2010. Lecture Notes in Computer Science, vol 6256. Springer, Berlin, Heidelberg

Abstract

In this paper we propose a new cause-effect non-symmetric measure applied to the task of Recognizing Textual Entailment .First we searched over a big corpus for sentences which contains the discourse marker “because” and collected cause-effect pairs. The entailment recognition is based on measure the cause-effect relation between the text and the hypothesis using the relative frequencies of words from the cause-effect pairs. Our measure outperformed the baseline method, over the three test sets of the PASCAL Recognizing Textual Entailment Challenges (RTE). The measure shows to be good at discriminate over the “true” class. Therefore we develop a meta-classifier using a symmetric measure and a non-symmetric measure as base classifiers. So, our meta-classifier has a competitive performance.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Miguel Angel Ríos Gaona
    • 1
  • Alexander Gelbukh
    • 1
  • Sivaji Bandyopadhyay
    • 2
  1. 1.Center for Computing ResearchNational Polytechnic InstituteMexico
  2. 2.Computer Science & Engineering DepartmentJadavpur UniversityKolkataIndia