Abstract:
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This paper describes an experimental comparison between two standard
supervised learning methods, namely Naive Bayes and Exemplar--based
classification, on the Word Sense Disambiguation (WSD) problem.
The aim of the work is twofold.
Firstly, it attempts to contribute to clarify some confusing information
about the comparison between both methods appearing in the related
literature. In doing so, several directions have been explored,
including: testing several modifications of the basic learning
algorithms and varying the feature space.
Secondly, an improvement of both algorithms is proposed, in order
to deal with large attribute sets.
This modification, which basically consists in using only the
positive information appearing in the examples,
allows to improve greatly the efficiency of the methods,
with no loss in accuracy.
The experiments have been performed on the largest sense--tagged
corpus available containing the most frequent and ambiguous English
words. Results show that the Exemplar--based approach to WSD
is generally superior to the Bayesian approach, especially when a
specific metric for dealing with symbolic attributes is used. |