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A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation

By Gerard Escudero, Lluis Marquez and German Rigau


This paper describes a set of comparative experiments, including cross--corpus evaluation, between five alternative algorithms for supervised Word Sense Disambiguation (WSD), namely Naive Bayes, Exemplar-based learning, SNoW, Decision Lists, and Boosting. Two main conclusions can be drawn: 1) The LazyBoosting algorithm outperforms the other four state-of-theart algorithms in terms of accuracy and ability to tune to new domains; 2) The domain dependence of WSD systems seems very strong and suggests that some kind of adaptation or tuning is required for cross--corpus application. 1 Introduction Word Sense Disambiguation (WSD) is the problem of assigning the appropriate meaning (or sense) to a given word in a text or discourse. Resolving the ambiguity of words is a central problem for large scale language understanding applications and their associate tasks (Ide and V'eronis, 1998). Besides, WSD is one of the most important open problems in NLP. Despite the wide range of approaches inve..

Topics: Decision Lists, and Boosting. Two main conclusions
Year: 2000
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