A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation

Details A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation

2000-09-22

arxiv.org/abs/cs/0009022v1

Proceedings of the 4th Conference on Computational Natural Language Learning, CoNLL'2000, pp. 31-36

Computer Science

Computation and Language

6 pages

Scientific paper

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-the-art 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.

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