Abstract
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.
Cite
CITATION STYLE
Escudero, G., Màrquez, L., & Rigau, G. (2000). A comparison between supervised learning algorithms for word sense disambiguation. In Proceedings of the 4th Conference on Computational Natural Language Learning, CoNLL 2000 and of the 2nd Learning Language in Logic Workshop, LLL 2000 - Held in cooperation with ICGI 2000 (pp. 31–36). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1117601.1117609
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