Abstract
We review in detail here a polished version of the systems with which we participated in the Senseval-2 competition English tasks (all words and lexical sample). It is based on a combination of selectional preference measured over a large corpus and hierarchical information taken from WordNet, as well as some additional heuristics. We use that information to expand sense glosses of the senses in WordNet and compare the similarity between the contexts vectors and the word sense vectors in a way similar to that used by Yarowsky and Schuetze. A supervised extension of the system is also discussed. We provide new and previously unpublished evaluation over the SemCor collection, which is two orders of magnitude larger than SENSEVAL-2 collections as well as comparison with baselines. Our systems scored first among unsupervised systems in both tasks. We note that the method is very sensitive to the quality of the characterizations of word senses; glosses being much better than training examples.
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References
Church, K.W., Hanks, P.: Word association norms, mutual information and lexicography. In: 27th Annual Conference of the Association of Computational Linguistics, pp. 76–82 (1989)
Cowie, J., Guthrie, J., Guthrie, L.: Lexical disambiguation using simulated annealing. In: International conference in computational linguistics (COLING), Nantes, pp. 359–365 (1992)
Dunning, T.E.: Accurate methods for the statistics of surprise and coincidence. Computational Linguistics 19(1), 61–74 (1993)
Fernández-Amorós, D., Gonzalo, J., Verdejo, F.: The role of conceptual relations in word sense disambiguation. In: Applications of Natural Language to Information Systems (NLDB), Madrid, pp. 87–98 (2001)
Fernández-Amorós, D., Gonzalo, J., Verdejo, F.: The uned systems at senseval-2. In: Proceedings of the 2nd International Workshop on Evaluating Word Sense Disambiguation Systems (SENSEVAL), Toulouse (2001)
Gale, W.A., Church, K.W., Yarowsky, D.: A method for disambiguating word senses in a large corpus. Computers and the Humanities 26(5), 415–439 (1993)
Lesk, M.E.: Automatic sense disambiguation using machine readable dictionaries : How to tell a pine cone from an ice cream cone. In: Proceedings of SIGDOC (Special Interest Group for Documentation Conference), Toronto, Canada (1986)
Schuetze, H., Pedersen, J.: Information retrieval based on word senses. In: Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, pp. 161–175 (1995)
Wilks, Y., Fass, D., Guo, C., McDonald, J., Plate, T., Slator, B.: Providing machine tractable dictionary tools. Machine Translation 5(2), 99–151 (1990)
Yarowsky, D.: Word-sense disambiguation using statistical models of Roget’s categories trained on large corpora. In: Proceedings of COLING 1992, Nantes, France, pp. 454–460 (1992)
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Fernandez-Amoros, D., Gil, R.H., Somolinos, J.A.C., Somolinos, C.C. (2010). Automatic Word Sense Disambiguation Using Cooccurrence and Hierarchical Information. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds) Natural Language Processing and Information Systems. NLDB 2010. Lecture Notes in Computer Science, vol 6177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13881-2_6
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DOI: https://doi.org/10.1007/978-3-642-13881-2_6
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