Advertisement

Mining Class Association Rules for Word Sense Disambiguation

  • Łukasz Kobyliński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7053)

Abstract

In this paper we propose an approach to the task of Word Sense Disambiguation problem that uses Class Association Rules to create an effective and human-understandable rule-based classifier. We present the accuracy of classification of selected polysemous words on an evaluation corpus using the proposed method and compare it to other known approaches. We discuss the advantages and weaknesses of a classifier based on association rules and present ideas for future work on the idea.

Keywords

Association Rule Frequent Itemsets Word Sense Class Association 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agirre, E., Edmonds, P. (eds.): Word Sense Disambiguation: Algorithms and Applications. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Agirre, E., Soroa, A.: Personalizing pagerank for word sense disambiguation. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009 (2009)Google Scholar
  3. 3.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, D.C., USA, pp. 207–216 (May 1993), citeseer.csail.mit.edu/agrawal93mining.html
  4. 4.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th Interntaional Conference on Very Large Data Bases, Santiago, Chile, pp. 487–499 (September 1994), citeseer.csail.mit.edu/agrawal94fast.html
  5. 5.
    Antonie, M.L., Zaïane, O.R.: Text document categorization by term association. In: Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM 2002, pp. 19–26. IEEE Computer Society, Washington, DC (2002), http://portal.acm.org/citation.cfm?id=844380.844745 CrossRefGoogle Scholar
  6. 6.
    Baś, D., Broda, B., Piasecki, M.: Towards word sense disambiguation of Polish. In: Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 73–78 (2008)Google Scholar
  7. 7.
    Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.): Proceedings of the Seventh International Conference on Language Resources and Evaluation, LREC 2010. ELRA, European Language Resources Association (ELRA), Valletta, Malta (May 2010)Google Scholar
  8. 8.
    Ide, N., Véronis, J.: Word sense disambiguation: The state of the art. Computational Linguistics 24(1), 1–40 (1998)Google Scholar
  9. 9.
    Kobyliński, Ł., Walczak, K.: Class association rules with occurrence count in image classification. TASK Quarterly 11(1–2), 35–45 (2007)Google Scholar
  10. 10.
    Lesk, M.: Automated sense disambiguation using machine-readable dictionaries: How to tell a pine cone from an ice cream cone. In: Proceedings of the 1986 SIGDOC Conference, Toronto, Canada (June 1986)Google Scholar
  11. 11.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, USA, August 27-31, pp. 80–86 (1998)Google Scholar
  12. 12.
    Mihalcea, R.: Co-training and self-training for word sense disambiguation. In: CoNLL 2004, Poznań, Poland (November 2004)Google Scholar
  13. 13.
    Młodzki, R., Przepiórkowski, A.: The WSD development environment. In: Vetulani, Z. (ed.) LTC 2009. LNCS, vol. 6562, pp. 224–233. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Ordonez, C., Omiecinski, E., Braal, L.d., Santana, C.A., Ezquerra, N., Taboada, J.A., Cooke, D., Krawczynska, E., Garcia, E.V.: Mining constrained association rules to predict heart disease. In: Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM 2001, pp. 433–440. IEEE Computer Society, Washington, DC (2001), http://portal.acm.org/citation.cfm?id=645496.658043 CrossRefGoogle Scholar
  15. 15.
    Paliouras, G., Karkaletsis, V., Androutsopoulos, I., Spyropoulos, C.D.: Learning rules for large-vocabulary word sense disambiguation: a comparison of various classifiers. In: Christodoulakis, D.N. (ed.) NLP 2000. LNCS (LNAI), vol. 1835, pp. 383–394. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  16. 16.
    Passonneau, R.J., Salleb-Aoussi, A., Bhardwaj, V., Ide, N.: Word sense annotation of polysemous words by multiple annotators. In: Calzolari, N., et al. [7]Google Scholar
  17. 17.
    Piasecki, M.: Polish tagger TaKIPI: Rule based construction and optimisation. Task Quarterly 11(1–2), 151–167 (2007)Google Scholar
  18. 18.
    Pradhan, S., Loper, E., Dligach, D., Palmer, M.: Semeval-2007 task-17: English lexical sample srl and all words. In: Proceedings of SemEval 2007 (2007)Google Scholar
  19. 19.
    Przepiórkowski, A., Górski, R.L., Łaziński, M., Pęzik, P.: Recent developments in the National Corpus of Polish. In: Calzolari, N., et al. [7]Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Łukasz Kobyliński
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarszawaPoland

Personalised recommendations