Learning Probabilistic Models of Word Sense Disambiguation

Details Learning Probabilistic Models of Word Sense Disambiguation Learning Probabilistic Models of Word Sense Disambiguation

2007-07-26

arxiv.org/abs/0707.3972v1

PhD dissertation, May 1998, Department of Computer Science and Engineering, Southern Methodist University

Computer Science

Computation and Language

195 pages

Scientific paper

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.

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