Vector-Based Unsupervised Word Sense Disambiguation for Large Number of Contexts

  • Gyula Papp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5729)


This paper presents a possible improvement of unsupervised word sense disambiguation (WSD) systems by extending the number of contexts applied by the discrimination algorithms. We carried out an experiment for several WSD algorithms based on the vector space model with the help of the SenseClusters ([1]) toolkit. Performances of algorithms were evaluated on a standard benchmark, on the nouns of the Senseval-3 English lexical-sample task ([2]). Paragraphs from the British National Corpus were added to the contexts of Senseval-3 data in order to increase the number of contexts used by the discrimination algorithms. After parameter optimization on Senseval-2 English lexical sample data performance measures show slight improvement, and the optimized algorithm is competitive with the best unsupervised WSD systems evaluated on the same data, such as [3].


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  1. 1.
    Purandare, A., Pedersen, T.: SenseClusters - finding clusters that represent word senses. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI 2004), San Jose, pp. 1030–3031 (2004)Google Scholar
  2. 2.
    Mihalcea, R., Chklovski, T., Kilgarriff, A.: The Senseval-3 English lexical sample task. In: Senseval-3 proceedings, pp. 25–28 (2004)Google Scholar
  3. 3.
    Agirre, E., Martínez, D., de Lacalle, O.L., Soroa, A.: Evaluating and optimizing the parameters of an unsupervised graph-based wsd algorithm. In: Proceedings of the TextGraphs Workshop: Graph-based algorithms for Natural Language Processing, New York, pp. 89–96 (2006)Google Scholar
  4. 4.
    Schütze, H.: Automatic word sense discrimination. Computational Linguistics 24(1), 97–123 (1998)Google Scholar
  5. 5.
    Véronis, J.: HyperLex: lexical cartography for information retrieval. Computer Speech & Language 18(3), 223–252 (2004)CrossRefGoogle Scholar
  6. 6.
    Purandare, A., Pedersen, T.: Word sense discrimination by clustering contexts in vector and similarity spaces. In: Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL), Boston, pp. 41–48 (2004)Google Scholar
  7. 7.
    Pedersen, T., Kulkarni, A.: Selecting the “right” number of senses based on clustering criterion functions. In: Proceedings of the Posters and Demo Program of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics, Trento, pp. 111–114 (2006)Google Scholar
  8. 8.
    Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: Proceedings of the 11th Conference of Information and Knowledge Management (CIKM), McLean, USA, pp. 515–524 (2002)Google Scholar
  9. 9.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a dataset via the Gap statistic. Journal of the Royal Statistics Society (Series B) 63(2), 411–423 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gyula Papp
    • 1
  1. 1.Faculty of Information Technology, Interdisciplinary Technical Sciences Doctoral SchoolPázmány Péter Catholic UniversityBudapestHungary

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