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.
@article{arxiv.cs/0009022,
title = {A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation},
author = {Gerard Escudero and Lluis Marquez and German Rigau},
journal= {arXiv preprint arXiv:cs/0009022},
year = {2007}
}