Taxonomic Dimensionality Reduction in Bayesian Text Classification

Lexical abstraction hierarchies can be leveraged to provide semantic information that characterizes features of text corpora as a whole. This information may be used to determine the classification utility of the dimensions that describe a dataset. This paper presents a new method for preparing a dataset for probabilistic classification by determining, a priori, the utility of a very small subset of taxonomically-related dimensions via a Discriminative Multinomial Naive Bayes process. We show that this method yields significant improvements over both Discriminative Multinomial Naive Bayes and Bayesian network classifiers alone.