  This dissertation presents several new methods of supervised and unsupervised
learning of word sense disambiguation models. The supervised methods focus on
performing model searches through a space of probabilistic models, and the
unsupervised methods rely on the use of Gibbs Sampling and the Expectation
Maximization (EM) algorithm. In both the supervised and unsupervised case, the
Naive Bayesian model is found to perform well. An explanation for this success
is presented in terms of learning rates and bias-variance decompositions.
