A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation

This paper describes a set of comparative experiments, including cross{corpus evaluation, between ve 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.

• Type: Journal
• Year: 2000
• Where: CORR
• Authors: Gerard Escudero, Lluís Màrquez, German Rigau
• Keywords: Algorithms, CORR 2000, Education, Naive Bayes, Word Sense Disambiguation