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Mining Class Association Rules for Word Sense Disambiguation

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Security and Intelligent Information Systems (SIIS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

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References

  1. Agirre, E., Edmonds, P. (eds.): Word Sense Disambiguation: Algorithms and Applications. Springer, Heidelberg (2006)

    Google Scholar 

  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. 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. 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. 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

    Chapter  Google Scholar 

  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. 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. Ide, N., Véronis, J.: Word sense disambiguation: The state of the art. Computational Linguistics 24(1), 1–40 (1998)

    Google Scholar 

  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. 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. 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. Mihalcea, R.: Co-training and self-training for word sense disambiguation. In: CoNLL 2004, Poznań, Poland (November 2004)

    Google Scholar 

  13. Młodzki, R., Przepiórkowski, A.: The WSD development environment. In: Vetulani, Z. (ed.) LTC 2009. LNCS, vol. 6562, pp. 224–233. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Piasecki, M.: Polish tagger TaKIPI: Rule based construction and optimisation. Task Quarterly 11(1–2), 151–167 (2007)

    Google Scholar 

  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. 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 

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Pascal Bouvry Mieczysław A. Kłopotek Franck Leprévost Małgorzata Marciniak Agnieszka Mykowiecka Henryk Rybiński

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Kobyliński, Ł. (2012). Mining Class Association Rules for Word Sense Disambiguation. In: Bouvry, P., Kłopotek, M.A., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybiński, H. (eds) Security and Intelligent Information Systems. SIIS 2011. Lecture Notes in Computer Science, vol 7053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25261-7_24

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  • DOI: https://doi.org/10.1007/978-3-642-25261-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25260-0

  • Online ISBN: 978-3-642-25261-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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