Paper: A Comparison Between Supervised Learning Algorithms For Word Sense Disambiguation

ACL ID W00-0706
Title A Comparison Between Supervised Learning Algorithms For Word Sense Disambiguation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000
Authors

This paper describes a set of comparative exper- iments, including cross-corpus evaluation, be- tween five alternative algorithms for supervised Word Sense Disambiguation (WSD), namely Naive Bayes, Exemplar-based learning, SNOW, Decision Lists, and Boosting. Two main conclu- sions can be drawn: 1) The LazyBoosting algo- rithm outperforms the other four state-of-the- art algorithms in terms of accuracy and ability to tune to new domains; 2) The domain depen- dence of WSD systems seems very strong and suggests that some kind of adaptation or tun- ing is required for cross-corpus application.