A Multisource Context-Dependent Semantic Distance Between Concepts

  • Ahmad El Sayed
  • Hakim Hacid
  • Djamel Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)


A major lack in the existing semantic similarity methods is that no one takes into account the context or the considered domain. However, two concepts similar in one context may appear completely unrelated in another context. In this paper, our first-level approach is context-dependent. We present a new method that computes semantic similarity in taxonomies by considering the context pattern of the text corpus. In addition, since taxonomies and corpora are interesting resources and each one has its strengths and weaknesses, we propose to combine similarity methods in our second-level multi-source approach. The performed experiments showed that our approach outperforms all the existing approaches.


Semantic Similarity Word Pair Latent Semantic Analysis Semantic Distance Text Corpus 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    And, N.S.: An intrinsic information content metric for semantic similarity in wordnetGoogle Scholar
  2. 2.
    Barsalou, L.: Intraconcept similarity and its application for interconcept similarity. Cambridge University Press, Cambridge (1989)Google Scholar
  3. 3.
    Christopher, H.S.: MANNING. Foundations of statistical natural language processing (1999)Google Scholar
  4. 4.
    Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. In: Proceedings of the 27th. Annual Meeting of the Association for Computational Linguistics, Vancouver, pp. 76–83. Association for Computational Linguistics (1989)Google Scholar
  5. 5.
    Dagan, I., Lee, L., Pereira, F.C.N.: Similarity-based models of word cooccurrence probabilities. Machine Learning 34(1-3), 43–69 (1999)zbMATHCrossRefGoogle Scholar
  6. 6.
    Furnas, G.W., Deerwester, S.C., Dumais, S.T., Landauer, T.K., Harshman, R.A., Streeter, L.A., Lochbaum, K.E.: Information retrieval using a singular value decomposition model of latent semantic structure. In: Chiaramella, Y. (ed.) SIGIR, pp. 465–480. ACM Press, New York (1988)Google Scholar
  7. 7.
    Hindle, D.: Noun classification from predicate-argument structures. In: Meeting of the Association for Computational Linguistics, pp. 268–275 (1990)Google Scholar
  8. 8.
    Hirst, G., St-Onge, D.: Lexical chains as representation of context for the detection and correction malapropisms (1997)Google Scholar
  9. 9.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy (1997)Google Scholar
  10. 10.
    Leacock, C., Chodorow, M., Miller, G.A.: Using corpus statistics and wordnet relations for sense identification. Computational Linguistics 24(1), 147–165 (1998)Google Scholar
  11. 11.
    Lin, D.: An information-theoretic definition of similarity. In: Proc. 15th International Conf. on Machine Learning, pp. 296–304. Morgan Kaufmann, San Francisco (1998)Google Scholar
  12. 12.
    Medin, D.: Psychological essentialism. Cambridge University Press, Cambridge (1989)Google Scholar
  13. 13.
    Miller, G.A.: Wordnet: A lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  14. 14.
    Miller, G.A., Charles, W.: Contextual correlated of semantic similarity. Language and Cognitive Processes 6, 1–28 (1991)CrossRefGoogle Scholar
  15. 15.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics 19(1), 17–30 (1989)CrossRefGoogle Scholar
  16. 16.
    Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. (JAIR) 11, 95–130 (1999)zbMATHGoogle Scholar
  17. 17.
    Turney, P.D.: Mining the Web for synonyms: PMI–IR versus LSA on TOEFL. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, p. 491. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. 18.
    Tversky, A.: Features of similarity. Psychological Review 84, 327–352 (1977)CrossRefGoogle Scholar
  19. 19.
    Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: 32nd. Annual Meeting of the Association for Computational Linguistics, New Mexico State University, Las Cruces, New Mexico, pp. 133–138 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ahmad El Sayed
    • 1
  • Hakim Hacid
    • 1
  • Djamel Zighed
    • 1
  1. 1.University of Lyon 2, ERIC Laboratory- 5, avenue Pierre Mendès-France, 69676 Bron cedexFrance

Personalised recommendations