Recognizing Textual Entailment with Statistical Methods

  • Miguel Angel Ríos Gaona
  • Alexander Gelbukh
  • Sivaji Bandyopadhyay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)


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.


Natural Language Processing Base Classifier Edit Distance Question Answering Dependency Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Corley, C., Mihalcea, R.: Measuring the semantic similarity of texts. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, pp. 13–18 (June 2005)Google Scholar
  2. 2.
    Dagan, I., Glickman, O.: Probabilistic textual entailment: Generic applied modeling of language variability. In: PASCAL workshop on Text Understanding (2004)Google Scholar
  3. 3.
    De Salvo Braz, R., Girju, R., Punyakanok, V., Frentiu, D.M.: An Inference Model for Word Sense Disambiguation. In: Proceedings of KEPT 2007, Knowledge Engineering Principles and Techniques, Workshop on Recognising Textual Entailment, vol. I (2007)Google Scholar
  4. 4.
    Glickman, O., Dagan, I., Koppel, M.: Web Based Probabilistic Textual Entailment. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2005)Google Scholar
  5. 5.
    Hobbs, J.R.: Ontological promiscuity. In: Proceedings of the 23rd annual meeting on Association for Computational Linguistics (1985)Google Scholar
  6. 6.
    Inkpen, D., Kipp, D., Nastase, V.: Machine Learning Experiments for Textual Entailment. In: Proceedings of the Second Challenge Workshop Recognising Textual Entailment, Venice, Italy (2006)Google Scholar
  7. 7.
    Kouylekov, M., Magnini, B.: Tree Edit Distance for Recognizing Textual Entailment: Estimating the Cost of Insertion. In: Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy (2006)Google Scholar
  8. 8.
    Pérez, D., Alfonseca, E.: Application of the Bleu algorithm for recognising textual entailments. In: Proceedings of the First Challenge Workshop Recognising Textual Etailment, Southampton, U.K., April 11-13, pp. 9–12 (2005)Google Scholar
  9. 9.
    Tatar, D., Gabriela, S., Andreea-Diana, M., Rada, M.: Textual Entailment as a Directional Relation. Journal of Research and Practice in Information Technology (2009)Google Scholar

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

Personalised recommendations