Unsupervised Word Sense Disambiguation with Lexical Chains and Graph-Based Context Formalization

  • Radu Ion
  • Dan Ştefănescu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6562)


This paper presents an unsupervised word sense disambiguation (WSD) algorithm that makes use of lexical chains concept [6] to quantify the degree of semantic relatedness between two words. Essentially, the WSD algorithm will try to maximize this semantic measure over a graph of content words in a given sentence in order to perform the disambiguation.


lexical chains graph-based WSD algorithm unsupervised WSD 


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  1. 1.
    Agirre, E., de Lacalle, O.L., Fellbaum, C., Hsieh, S.K., Tesconi, M., Monachini, M., Vossen, P., Segers, R.: SemEval-2010 Task 17: All-wordsWord Sense Disambiguation on a Specific Domain. In: Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, Uppsala, Sweden, July 15-16, pp. 75–80. Association for Computational Linguistics (2010)Google Scholar
  2. 2.
    Fellbaum, C. (ed.): WordNet: an Electronic Lexical Database. MIT Press, Cambridge (1998)MATHGoogle Scholar
  3. 3.
    Galley, M., McKeown, K.: Improving word sense disambiguation in lexical chaining. In: Proceedings of 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico, August 9-15 (2003)Google Scholar
  4. 4.
    Ion, R., Tufiş, D.: RACAI: Meaning Affinity Models. In: Agirre, E., Màrquez, L., Wicentowski, R. (eds.) Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval 2007), Prague, Czech Republic, pp. 282–287. Association for Computational Linguistics (June 2007)Google Scholar
  5. 5.
    Miller, G.A., Leacock, C., Tengi, R., Bunker, R.T.: A semantic concordance. In: Proceedings of the 3rd DARPA Workshop on Human Language Technology, Plainsboro, New Jersey, pp. 303–308 (1993)Google Scholar
  6. 6.
    Moldovan, D., Novischi, A.: Lexical chains for question answering. In: Proceedings of the 19th International Conference on Computational Linguistics, Taipei, Taiwan, August 24-September 01, pp. 1–7 (2002)Google Scholar
  7. 7.
    Navigli, R., Lapata, M.: An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 32(4), 678–692 (2010)CrossRefGoogle Scholar
  8. 8.
    Pradhan, S.S., Loper, E., Dligach, D., Palmer, M.: SemEval-2007 Task 17: English Lexical Sample, SRL and All Words. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval 2007), Prague, pp. 87–92. Association for Computational Linguistics (June 2007)Google Scholar
  9. 9.
    Silber, H.G., McCoy, K.F.: Efficiently computed lexical chains as an intermediate representation for automatic text summarization. Computational Linguistics 28(4), 487–496 (2002)CrossRefGoogle Scholar
  10. 10.
    Sinha, R., Mihalcea, R.: Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity. In: Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2007), Irvine, CA (September 2007)Google Scholar
  11. 11.
    Tufiş, D., Ion, R., Ceauşu, A., Ştefănescu, D.: RACAI’s Linguistic Web Services. In: Proceedings of the 6th Language Resources and Evaluation Conference – LREC 2008, Marrakech, Morocco, ELRA – European Language Resources Association (May 2008) ISBN 2-9517408-4-0Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Radu Ion
    • 1
  • Dan Ştefănescu
    • 1
  1. 1.Research Institute for Artificial Intelligence of the Romanian AcademyRomania

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