  In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied
to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of
15 selected polysemous words show that the boosting approach surpasses Naive
Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy
on supervised WSD. In order to make boosting practical for a real learning
domain of thousands of words, several ways of accelerating the algorithm by
reducing the feature space are studied. The best variant, which we call
LazyBoosting, is tested on the largest sense-tagged corpus available containing
192,800 examples of the 191 most frequent and ambiguous English words. Again,
boosting compares favourably to the other benchmark algorithms.
