Naive Bayes as a Satis cing Model

We report on an empirical study of supervised learning algorithms that induce models to resolve the meaning of ambiguous words in text. We nd that the Naive Bayesian classi er is as accurate as several more sophisticated methods. This is a surprising result since Naive Bayes makes simplifying assumptions about disambiguation that are not realistic. However, our results correspond to a growing body of evidence that Naive Bayes acts as a satis cing model in a wide range of domains. We suggest that bias variance decompositions of classi cation error can be used to identify and develop satis cing models.