Combining Lexical Resources with Tree Edit Distance for Recognizing Textual Entailment

  • Milen Kouylekov
  • Bernardo Magnini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3944)


This paper addresses Textual Entailment (i.e. recognizing that the meaning of a text entails the meaning of another text) using a Tree Edit Distance algorithm between the syntactic trees of the two texts. A key aspect of the approach is the estimation of the cost for the editing operations (i.e. Insertion, Deletion, Substitution) among words.

The aim of the paper is to compare the contribution of two different lexical resources for recognizing textual entailment: WordNet and a word-similarity database. In both cases we derive entailment rules that are used by the Tree Edit Distance Algorithm. We carried out a number of experiments over the PASCAL-RTE dataset in order to estimate the contribution of different combinations of the available resources.


Question Answering Dependency Tree Edit Operation Statistical Machine Translation Entailment Relation 


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  1. 1.
    Bayer, S., Burger, J., Ferro, L., Henderson, J., Yeh, A.: MITRE’s Submissions to the EU Pascal RTE Challenge. In: Proceedings of PASCAL Workshop on Recognizing Textual Entailment, Southampton, UK (2005)Google Scholar
  2. 2.
    Budanitsky, A., Hirst, G.: Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In: Workshop on WordNet and other Lexical Resources, Second meeting of the Nord American Chapter of the Association for Computational Linguistics, Pittsburgh (2001)Google Scholar
  3. 3.
    Dagan, I., Glickman, O.: Generic applied modeling of language variability. In: Proceedings of PASCAL Workshop on Learning Methods for Text Understanding and Mining, Grenoble (2004)Google Scholar
  4. 4.
    Dagan, I., Glickman, O., Magnini, B.: The PASCAL Recognizing Textual Entailment Challenge. In: Proceedings of PASCAL Workshop on Recognizing Textual Entailment, Southampton, UK (2005)Google Scholar
  5. 5.
    Fellbaum, C.: WordNet, an electronic lexical database. MIT Press, Cambridge (1998)MATHGoogle Scholar
  6. 6.
    Harabagiu, S., Miller, G., Moldovan, D.: WordNet 2 - A morphologically and Semantically Enhanced Resource. In: Proceeding of ACL-SIGLEX 1999, Marylend (1999)Google Scholar
  7. 7.
    Herrera, J., Peñas, A., Verdejo, F.: Textual Entailment Recognition Based on Dependency Analysis and WordNet. In: Proceedings of PASCAL Workshop on Recognizing Textual Entailment, Southampton, UK (2005)Google Scholar
  8. 8.
    Jijkoun, V., de Rijke, M.: Recognizing Textual Entailment Using Lexical Similarity. In: Proceedings of PASCAL Workshop on Recognizing Textual Entailment Southampton, UK (2005)Google Scholar
  9. 9.
    Lin, D.: Dependency-based evaluation of MINIPAR. In: Proceedings of the Workshop on Evaluation of Parsing Systems at LREC 1998, Granada, Spain (1998)Google Scholar
  10. 10.
    Lin, D.: An Information-Theoretic Definition of Similarity. In: Proceedings of International Conference on Machine Learning, Madison, Wisconsin (July 1998)Google Scholar
  11. 11.
    Lin, D., Pantel, P.: Discovery of inference rules for Question Answering. Natural Language Engineering 7(4), 343–360 (2001)CrossRefGoogle Scholar
  12. 12.
    Moldovan, D., Rus, V.: Logic Form Transformation and it’s Applicability in Question Answering. In: Proceedings of ACL 2001 (2001)Google Scholar
  13. 13.
    Moldovan, D., Harabagio, S., Girju, R., Morarescu, P., Lacatsu, F., Novischi, A.: A LCC Tools for Question Answering. In: NIST Special Publication: SP 500-251 The Eleventh Text Retrieval Conference (TREC 2002-2003)Google Scholar
  14. 14.
    Monz, C., de Rijke, M.: Light-Weight Entailment Checking for Computational Semantics. In: The third workshop on inference in computational semantics (ICoS-3) (2001)Google Scholar
  15. 15.
    Pedersen, T., Patwardhan, S., Michelizzi, J.: WordNet:Similarity- Measuring the relatedness of concepts. In: AAAI 2004 (2004)Google Scholar
  16. 16.
    Punyakanok, V., Roth, D., Yih, W.-t.: Mapping Dependencies Trees: An Application to Question Answering. In: Proceedings of AI & Math (2004)Google Scholar
  17. 17.
    Raina, R., Haghighi, A., Cox, C., Finkel, J., Michels, J., Toutanova Bill MacCartney, K., de Marneffe, M.-C., Manning, C.D., Ng, A.Y.: Robust Textual Inference using Diverse Knowledge Sources. In: Proceedings of PASCAL Workshop on Recognizing Textual Entailment, Southampton, UK (2005)Google Scholar
  18. 18.
    Ratnaparkhi, A.: A Maximum Entropy Part-Of-Speech Tagger. In: Proceeding of the Empirical Methods in Natural Language Processing Conference, May 17-18 (1996)Google Scholar
  19. 19.
    Szpektor, I., Tanev, H., Dagan, I., Coppola, B.: Scaling Web-based Acquisition of Entailment Relations. In: Proceedings of EMNLP 2004 – Empirical Methods in Natural Language Processing, Barcelona (July 2004)Google Scholar
  20. 20.
    Zhang, K., Shasha, D.: Fast algorithm for the unit cost editing distance between trees. Journal of Algorithms 11, 1245–1262 (1990)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Milen Kouylekov
    • 1
    • 2
  • Bernardo Magnini
    • 1
  1. 1.ITC-irst, Centro per la Ricerca Scientifica e TecnologicaPovo, TrentoItaly
  2. 2.University of TrentoPovo, TrentoItaly

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