Recognizing Textual Entailment Using a Machine Learning Approach

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

DOI: 10.1007/978-3-642-16773-7_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)
Cite this paper as:
Ríos Gaona M.A., Gelbukh A., Bandyopadhyay S. (2010) Recognizing Textual Entailment Using a Machine Learning Approach. In: Sidorov G., Hernández Aguirre A., Reyes García C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science, vol 6438. Springer, Berlin, Heidelberg

Abstract

We present our experiments on Recognizing Textual Entailment based on modeling the entailment relation as a classification problem. As features used to classify the entailment pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross validation) and accuracy of 63% on the RTE-3 test dataset.

Keywords

Recognizing Textual Entailment text similarity measures non-symmetric measures 

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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

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