The Usefulness of Conceptual Representation for the Identification of Semantic Variability Expressions

  • Zornitsa Kozareva
  • Sonia Vázquez
  • Andrés Montoyo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)


The need of the current Natural Language Processing applications to identify text segments that express the same meaning in different ways, evolved into the identification of semantic variability expressions. Most of the developed approaches focus on the text structure, such as the word overlaps, the distance between phrases or syntactic trees, word to word similarity, logic representation among others. However, current research did not identify how the global conceptual representation of a sentences can contribute to the resolution of this problem. In this paper, we present an approach where the meaning of a sentence is represented with the associated relevant domains. In order to determine the semantic relatedness among text segments, Latent Semantic Analysis is used. We demonstrate, evaluate and analyze the contribution of our conceptual representation approach in an evaluation with the paraphrase task.


Mutual Information Latent Semantic Analysis Conceptual Representation Syntactic Category Word Sense Disambiguation 
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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  1. 1.
    Barzilay, R., McKeown, K.: Extracting paraphrases from a parallel corpus. In: ACL 2001, pp. 50–57 (2001)Google Scholar
  2. 2.
    Barzilay, R., McKeown, K.: Learning to paraphrase: An unsupervised approach using multiple-sequence alignment. In: HTLT-NAACL 2003, pp. 16–23 (2003)Google Scholar
  3. 3.
    Corley, C., Mihalcea, R.: Measures of text semantic similarity. In: Proceedings of the ACL workshop on Empirical Modeling of Semantic Equivalence (2005)Google Scholar
  4. 4.
    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
  5. 5.
    Dolan, B., Quirk, C., Brockett, C.: Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources. In: Proceedings of the 20th International Conference on Computational Linguistics, Geneva, Switzerland (2004)Google Scholar
  6. 6.
    FellBaum, C.: WordNet, an electronic lexical database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  7. 7.
    Gonzalo, J., Verdejo, F., Peters, C., Calzolari, N.: Applying eurowordnet to cross-language text retrieval. pp. 113–135 (1998)Google Scholar
  8. 8.
    Kouylekov, M., Magnini, B.: Tree edit distance for recognizing textual entailment: Estimating the cost of insertion. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment, pp. 17–20 (2006)Google Scholar
  9. 9.
    Kozareva, Z., Montoyo, A.: Paraphrase identification on the basis of supervised machine learning techniques. In: FinTAL, pp. 524–533 (2006)Google Scholar
  10. 10.
    Landauer, T., Dumais, S.: A solution to plato’s problem: The latent semantic analysis theory of acquisition. Psychological Review, 211–240 (1997)Google Scholar
  11. 11.
    Lin, D., Pantel, P.: Discovery of inference rules for question answering. Natural Language Engineering 4(7), 343–360Google Scholar
  12. 12.
    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
  13. 13.
    Magnini, B., Strapparava, C., Pezzulo, G., Gliozzo, A.: Using domain information for word sense disambiguation. In: SENSEVAL-2 (2001)Google Scholar
  14. 14.
    Muñoz, R., Montoyo, A.: Definite description resolution enrichment with wordnet domain labels. In: IBERAMIA, pp. 645–654 (2002)Google Scholar
  15. 15.
    Niles, I., Pease, A.: Linking lexicons and ontologies: Mapping wordnet to the suggested upper merged ontology. In: Proceedings of the 2003 International Conference on Information and Knowledge Engineering (IKE 03). Las Vegas, Nevada (2003)Google Scholar
  16. 16.
    Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: International Conference on New Methods in Language Processing, Manchester, UK (1994)Google Scholar
  17. 17.
    Stevenson, M., Greenwood, M.A.: Learning information extraction patterns using wordnet. In: Proceedings of the 3rd International Conference of the Global WordNet Association (GWA’06) (2006)Google Scholar
  18. 18.
    Szpektor, I., Tanev, H., Dagan, I., Coppola, B.: Scaling web-based acquisition of entailment relations. In: Proceedings of Empirical Methods in Natural Language Processing (2004)Google Scholar
  19. 19.
    Vázquez, S., Montoyo, A., Rigau, G.: Using relevant domains resource for word sense disambiguation. In: IC-AI, pp. 784–789 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zornitsa Kozareva
    • 1
  • Sonia Vázquez
    • 1
  • Andrés Montoyo
    • 1
  1. 1.Departamento de Lenguajes y Sistemas Informáticos, Universidad de AlicanteSpain

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