A mapping between classifiers and training conditions for WSD

Authors:
Aarón Pancardo-Rodríguez;Manuel Montes-y-Gómez;Luis Villaseñor-Pineda;Paolo Rosso
Affiliations:
National Institute of Astrophysics, Optics and Electronics, Mexico;National Institute of Astrophysics, Optics and Electronics, Mexico;National Institute of Astrophysics, Optics and Electronics, Mexico;Polytechnic University of Valencia, Spain
Venue:
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Year:
2005

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Learning Rules for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various Classifiers

NLP '00 Proceedings of the Second International Conference on Natural Language Processing

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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.