Abstract
This paper presents a method of improving the results of automatic Word Sense Disambiguation by generalizing nouns appearing in a disambiguated context to concepts. A corpus-based semantic similarity function is used for that purpose, by substituting appearances of particular nouns with a set of the most closely related similar words. We show that this approach may be applied to both supervised and unsupervised WSD methods and in both cases leads to an improvement in disambiguation accuracy. We evaluate the proposed approach by conducting a series of lexical sample WSD experiments on both domain-restricted dataset and a general, balanced Polish-language text corpus.
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References
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)
Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)
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, pp. 33–41. ACL (2009)
Kopeć, M., Młodzki, R., Przepiórkowski, A.: Word Sense Disambiguation in the National Corpus of Polish. Prace Filologiczne, vol. LX (forthcoming, 2012)
Kobyliński, Ł.: Mining Class Association Rules for Word Sense Disambiguation. In: Bouvry, P., Kłopotek, M.A., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybiński, H. (eds.) SIIS 2011. LNCS, vol. 7053, pp. 307–317. Springer, Heidelberg (2012)
Kohomban, U.S., Lee, W.S.: Learning semantic classes for word sense disambiguation. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 34–41. ACL (2005)
Banerjee, S., Pedersen, T.: An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002)
Iida, R., McCarthy, D., Koeling, R.: Gloss-based semantic similarity metrics for predominant sense acquisition. In: Proceedings of the Third International Joint Conference on Natural Language Processing, pp. 561–568 (2008)
Lin, D.: Automatic retrieval and clustering of similar words. In: COLING-ACL, pp. 768–774 (1998)
Budanitsky, A., Hirst, G.: Evaluating WordNet-based measures of lexical semantic relatedness. Computational Linguistics 32, 13–47 (2006)
Piasecki, M., Szpakowicz, S., Broda, B.: Automatic Selection of Heterogeneous Syntactic Features in Semantic Similarity of Polish Nouns. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 99–106. Springer, Heidelberg (2007)
Młodzki, R., Przepiórkowski, A.: The WSD development environment. In: Proceedings of the 4th Language and Technology Conference (2009)
Przepiórkowski, A., Bańko, M., Górski, R.L., Lewandowska-Tomaszczyk, B. (eds.): Narodowy Korpus Jc̨zyka Polskiego. Wydawnictwo Naukowe PWN, Warsaw (forthcoming)
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Kobyliński, Ł., Kopeć, M. (2012). Semantic Similarity Functions in Word Sense Disambiguation. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2012. Lecture Notes in Computer Science(), vol 7499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32790-2_3
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DOI: https://doi.org/10.1007/978-3-642-32790-2_3
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