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Textual Entailment Beyond Semantic Similarity Information

  • Sonia Vázquez
  • Zornitsa Kozareva
  • Andrés Montoyo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

The variability of semantic expression is a special characteristic of natural language. This variability is challenging for many natural language processing applications that try to infer the same meaning from different text variants. In order to treat this problem a generic task has been proposed: Textual Entailment Recognition. In this paper, we present a new Textual Entailment approach based on Latent Semantic Indexing (LSI) and the cosine measure. This proposed approach extracts semantic knowledge from different corpora and resources. Our main purpose is to study how the acquired information can be combined with an already developed and tested Machine Learning Entailment system (MLEnt). The experiments show that the combination of MLEnt, LSI and cosine measure improves the results of the initial approach.

Keywords

Word Sense Semantic Space Question Answering Relevant Domain Latent Semantic Indexing 
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.

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References

  1. 1.
    Kozareva, Z., Montoyo, A.: The role and the resolution of textual entailment for natural language processing applications. In: 11th International Conference on Applications of Natural Language to Information Systems (NLDB) (2006)Google Scholar
  2. 2.
    Dagan, I., Glickman, O., Magnini, B.: The pascal recognising textual entailment challenge. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2005)Google Scholar
  3. 3.
    Dagan, I., Glickman, O.: Probabilistic textual entailment: Generic applied modeling of language variability. In: PASCAL Workshop on Learning Methods for Text Understanding and Mining (2004)Google Scholar
  4. 4.
    Akhmatova, E.: Textual entailment resolution via atomic propositions. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment, pp. 61–64 (2005)Google Scholar
  5. 5.
    Herrera, J., Peñas, A., Verdejo, F.: Textual entailment recognition based on dependency analysis and wordnet. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2005)Google Scholar
  6. 6.
    Jijkoun, V., de Rijke, M.: Recognizing textual entailment using lexical similarity. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2005)Google Scholar
  7. 7.
    Montes, M., Gelbukh, A., López, A., Baeza-Yates, R.: Flexible comparison of conceptual graphs. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 102–111. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic indexing. Journal of the American Society for Information Science 41, 321–407 (1990)CrossRefGoogle Scholar
  9. 9.
    Magnini, B., Cavaglia, G.: Integrating Subject Field Codes into WordNet. In: Gavrilidou, M., Crayannis, G., Markantonatu, S., Piperidis, S., Stainhaouer, G. (eds.) Proceedings of LREC-2000, Second International Conference on Language Resources and Evaluation, Athens, Greece, pp. 1413–1418 (2000)Google Scholar
  10. 10.
    Vázquez, S., Montoyo, A., Rigau, G.: Using relevant domains resource for word sense disambiguation. In: IC-AI, pp. 784–789 (2004)Google Scholar
  11. 11.
    Kozareva, Z., Montoyo, A.: Mlent: The machine learning entailment system of the university of alicante. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2006)Google Scholar
  12. 12.
    Aston, G.: The british national corpus as a language learner resource. In: TALC 1996 (1996)Google Scholar
  13. 13.
    Church, K., Hanks, P.: Word association norms, mutual information and lexicograhy. Computational Lingüistics 16, 22–29 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sonia Vázquez
    • 1
  • Zornitsa Kozareva
    • 1
  • Andrés Montoyo
    • 1
  1. 1.Department of Software and Computing SystemsUniversity of Alicante 

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