A Global Ant Colony Algorithm for Word Sense Disambiguation Based on Semantic Relatedness

  • Didier Schwab
  • Nathan Guillaume
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 89)


Brute-force WSD algorithms based on semantic relatedness are really time consuming. We study how to perform faster and better WSD. We focus here on an ant colony algorithm and evaluate it to exhibit some of its characteristics.


Travelling Salesman Problem Semantic Relatedness Machine Translation Local Algorithm Word Sense 
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.
    Agirre, E., Edmonds, P.: Word Sense Disambiguation: Algorithms and Applications (Text, Speech and LT). Springer-Verlag New York, Inc., Secaucus (2006)CrossRefGoogle Scholar
  2. 2.
    Banerjee, S., Pedersen, T.: An adapted lesk algorithm for word sense disambiguation using wordnet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Cowie, J., Guthrie, J., Guthrie, L.: Lexical disambiguation using simulated annealing. In: COLING 1992, Nantes, France, vol. 1, pp. 359–365 (1992)Google Scholar
  4. 4.
    Cramer, I., Wandmacher, T., Waltinger, U.: Modeling, Learning and Processing of Text Technological Data Structures. In: WordNet: An Electronic Lexical Database. Springer, Heidelberg (2010)Google Scholar
  5. 5.
    Dorigo, S.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  6. 6.
    Falaise, A., Rouquet, D., Schwab, D., Blanchon, H., Boitet, C.: Extraction de contenu seímantique dans des masses de donneíes multilingues. le projet omnia. TAL 52(1), 11–19 (2010)Google Scholar
  7. 7.
    Gale, W., Church, K., Yarowsky, D.: One sense per discourse. In: Fifth DARPA Speech and Natural Language Workshop, Harriman, New-York, États-Unis, pp. 233–237 (1992)Google Scholar
  8. 8.
    Gelbukh, A., Sidorov, G., Han, S.Y.: Evolutionary approach to natural language wsd through global coherence optimization. WSEAS Transactions on Communications 2(1), 11–19 (2003)Google Scholar
  9. 9.
    Guinand, F., Lafourcade, M.: Artificial Ants. From Collective Intelligence to Real-life Optimization and Beyond. In: Artificial ants for NLP, Lavoisier, ch. 20, pp. 455–492 (2009)Google Scholar
  10. 10.
    Ide, N., Véronis, J.: Wsd: the state of the art. Computational Linguistics 28(1), 1–41 (1998)Google Scholar
  11. 11.
    Lafourcade, M., Schwab, D.: Multi-castes ants for holistic semantic text analysis. In: SNLP 2005: The 6th Symposium on NLP, Chiang Rai, Thailand (2005)Google Scholar
  12. 12.
    Lesk, M.: Automatic sense disambiguation using mrd: how to tell a pine cone from an ice cream cone. In: Proceedings of SIGDOC 1986, pp. 24–26. ACM, New York (1986)CrossRefGoogle Scholar
  13. 13.
    Navigli, R.: Wsd: a survey. ACM Computing Surveys 41(2), 1–69 (2009)CrossRefGoogle Scholar
  14. 14.
    Navigli, R., Litkowski, K.C., Hargraves, O.: Semeval-2007 task 07: Coarse-grained english all-words task. In: SemEval 2007, Prague, Czech Republic, pp. 30–35 (2007)Google Scholar
  15. 15.
    Pedersen, T., Banerjee, S., Patwardhan, S.: Maximizing Semantic Relatedness to Perform WSD. Research report, University of Minnesota Supercomputing Institute (2005)Google Scholar
  16. 16.
    Schwab, D.: Approche hybride pour la modélisation, la détection et l’exploitation des fonctions lexicales en vue de l’analyse sémantique de texte. Ph.D. thesis, U. Montpellier 2 (2005)Google Scholar
  17. 17.
    Vasilescu, F., Langlais, P., Lapalme, G.: Evaluating variants of the lesk approach for disambiguating words. In: Proceedings of LREC 2004, Lisbon, Portugal, pp. 633–636 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Didier Schwab
    • 1
  • Nathan Guillaume
    • 1
  1. 1.LIG-GETALP (Laboratory of Informatics of Grenoble - Study Group for Machine, Translation and Automated Processing of Languages and Speech)University of GrenobleFrance

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