Using Sentence Semantic Similarity Based on WordNet in Recognizing Textual Entailment

  • Julio J. Castillo
  • Marina E. Cardenas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6433)


This paper presents a Recognizing Textual Entailment system which uses semantic distances to sentence level over WordNet to assess the impact on predicting Textual Entailment datasets. We extent word-to-word metrics to sentence level in order to best fit in textual entailment domain. Finally, we show experiments over several RTE datasets and draw conclusions about the useful of WordNet semantic measures on this task. As a conclusion, we show that an initial but average-score system can be built using only semantic information from WordNet.


Recognizing Textual Entailment WordNet Semantic Similarity 


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  1. 1.
    Giampiccolo, D., Dang, H., Magnini, B., Dagan, I., Cabrio, E.: The Fourth PASCAL Recognizing Textual Entailment Challenge. In: Proceedings TAC 2008 (2008)Google Scholar
  2. 2.
    Bentivogli, L., Dagan, I., Dang, H., Giampiccolo, D., Magnini, B.: The Fifth PASCAL Recognizing Textual Entailment Challenge. In: Proceedings TAC 2009 (2009)Google Scholar
  3. 3.
    Herrera, J., Penas, A., Verdejo, F.: Textual Entailment Recognition Based on Dependency Analysis and WordNet. PASCAL. In: First Challenge Workshop (2005)Google Scholar
  4. 4.
    Ofoghi, B., Yearwood, J.: From Lexical Entailment to Recognizing Textual Entailment Using Linguistic Resources. In: Australasian Language Technology Association Workshop (2009)Google Scholar
  5. 5.
    Castillo, J.: A Machine Learning Approach for Recognizing Textual Entailment in Spanish. In: NAACL, Los Angeles, USA (2010)Google Scholar
  6. 6.
    Li, Y., McLean, D., Bandar, Z., O’Shea, J., Crockett, K.: Sentence Similarity based on Semantic Nets and Corpus Statistics. IEEE TKDE 18(8), 1138–1150 (2006)Google Scholar
  7. 7.
    Li, Y., Bandar, A., McLean, D.: An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE TKDE 15(4), 871–882 (2003)Google Scholar
  8. 8.
    Wiemer-Hastings, P.: Adding Syntactic Information to LSA. In: Proc. 22nd Ann. Conf. Cognitive Science Soc., pp. 989–993 (2000)Google Scholar
  9. 9.
    Landauer, T., Foltz, P., Laham, D.: Introduction to Latent Semantic Analysis. Discourse Processes 25(2-3), 259–284 (1998)CrossRefGoogle Scholar
  10. 10.
    Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  11. 11.
    Castillo, J.: Using Machine Translation Systems to Expand a Corpus in Textual Entailment. In: Icetal 2010, Reykjavik, Iceland (2010)Google Scholar
  12. 12.
    Resnik, P.: Information Content to Evaluate Semantic Similarity in a Taxonomy. In: Proc. of IJCAI 1995, pp. 448–453 (1995)Google Scholar
  13. 13.
    Lin, D.: An Information-Theoretic Definition of Similarity. In: Proc. of Conf. on Machine Learning, pp. 296–304 (1998)Google Scholar
  14. 14.
    Jiang, J., Conrath, D.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: Proc. ROCLING X (1997)Google Scholar
  15. 15.
    Pirrò, G., Seco, N.: Design, Implementation and Evaluation of a New Similarity Metric Combining Feature and Intrinsic Information Content. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Castillo, J.: Recognizing Textual Entailment: Experiments with Machine Learning Algorithms and RTE Corpora. In: Cicling 2010, Iaşi, Romania (2010)Google Scholar
  17. 17.
    Lesk, L.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from a ice cream cone. In: Proceedings of SIGDOC 1986 (1986)Google Scholar
  18. 18.
    Kuhn, H.: The Hungarian Method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Levenshtein, V.: Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady 10, 707 (1966)MathSciNetMATHGoogle Scholar
  20. 20.
    Zanzotto, F., Pennacchiotti, M., Moschitti, A.: Shallow Semantics in Fast Textual Entailment Rule Learners, RTE3, Prague (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Julio J. Castillo
    • 1
    • 2
  • Marina E. Cardenas
    • 2
  1. 1.National University of Cordoba-FaMAFCordobaArgentina
  2. 2.National Technological University-FRCCordobaArgentina

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