Abstract
This chapter describes the problem we are investigating and trying to solve in all other chapters. It introduces word sense disambiguation (WSD) and Naïve Bayes-based WSD, as well as local type features for unsupervised WSD with an underlying Naïve Bayes model.
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Notes
- 1.
Especially with reference to supervised WSD.
- 2.
The context window can be of fixed size or it can be represented by the entire sentence in which the target word occurs.
- 3.
Available at http://wordnet.princeton.edu/.
- 4.
For more details concerning these types of features, feature sets, and their usage, see (Pedersen and Bruce 1998).
References
Agirre, E., Edmonds, P. (eds.): Word Sense Disambiguation. Algorithms and Applications. Springer, New York (2006)
Bruce, R., Wiebe, J., Pedersen, T.: The measure of a model. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 101–112. Philadelphia, PA (1996)
Eberhardt, F., Danks, D.: Confirmation in the cognitive sciences: the problematic case of Bayesian models. Mind. Mach. 21, 389–410 (2011)
Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. The MIT Press, Cambridge (1998)
Hristea, F.: Recent advances concerning the usage of the Naïve Bayes model in unsupervised word sense disambiguation. Int. Rev. Comput. Softw. 4(1), 58–67 (2009)
Hristea, F., Popescu, M., Dumitrescu, M.: Performing word sense disambiguation at the border between unsupervised and knowledge-based techniques. Artif. Intell. Rev. 30(1), 67–86 (2008)
Hristea, F., Popescu, M.: Adjective sense disambiguation at the border between unsupervised and knowledge-based techniques. Fundam. Inf. 91(3–4), 547–562 (2009)
Leacock, C., Towell, G., Voorhees, E.: Corpus-based statistical sense resolution. In: Proceedings of the ARPA Workshop on Human Language Technology, pp. 260–265. Princeton, New Jersey (1993)
Miller, G.A.: Nouns in WordNet: a lexical inheritance system. Int. J. Lexicogr. 3(4), 245–264 (1990)
Miller, G.A.: WordNet: a lexical database. Commun. ACM 38(11), 39–41 (1995)
Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: WordNet: an on-line lexical database. J. Lexicogr. 3(4), 234–244 (1990)
Pedersen, T.: Unsupervised corpus-based methods for WSD. In: Agirre, E., Edmonds, P. (eds.) Word Sense Disambiguation. Algorithms and Applications. Springer, New York (2006)
Pedersen, T., Bruce, R.: Distinguishing word senses in untagged text. In: Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP-2), pp. 197–207. Providence, Rhode Island (1997)
Pedersen, T., Bruce, R.: Knowledge lean word-sense disambiguation. In: Proceedings of the 15th National Conference on Artificial Intelligence, pp. 800–805. Madison, Wisconsin (1998)
Ponzetto, S.P., Navigli, R.: Knowledge-rich word sense disambiguation rivaling supervised systems. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), pp. 1522–1531. ACL Press, Uppsala, Sweden (2010)
Preoţiuc-Pietro, D., Hristea, F.: Unsupervised word sense disambiguation with N-gram features. Artif. Intell. Rev. (2012). doi:10.1007/s10462-011-9306-y
Schütze, H.: Automatic word-sense discrimination. Comput. Linguist. 24(1), 97–123 (1998)
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Hristea, F.T. (2013). Preliminaries . In: The Naïve Bayes Model for Unsupervised Word Sense Disambiguation. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33693-5_1
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