Abstract
This paper studies performance of various classifiers for Word Sense Disambiguation considering different training conditions. Our preliminary results indicate that the number and distribution of training examples has a great impact on the resulting precision. The Naïve Bayes method emerged as the most adequate classifier for disambiguating words having few examples.
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Pancardo-Rodríguez, A., Montes-y-Gómez, M., Villaseñor-Pineda, L., Rosso, P. (2005). A Mapping Between Classifiers and Training Conditions for WSD. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_27
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DOI: https://doi.org/10.1007/978-3-540-30586-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24523-0
Online ISBN: 978-3-540-30586-6
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