Learning Probabilistic Models of Word Sense Disambiguation
arXiv:0707.3972
Abstract
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.
195 pages
References in corpus (4)
- Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
- Distinguishing Word Senses in Untagged Text
- Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach
- Sequential Model Selection for Word Sense Disambiguation