A Characterization of WordNet Features in Boolean Models for Text Classification
Supervised text classification is the task of automatically assigning a category label to a previously unlabeled text document. The authors start with a collection of pre-labeled examples whose assigned categories are used to build a predictive model for each category. In previous research, incorporating semantic features from the WordNet lexical database is one of many approaches that have been tried to improve the predictive accuracy of text classification models. The intuition is that words in the training set alone may not be extensive enough to enable the generation of a universal model for a category, but through WordNet expansion (i.e., incorporating words defined by various relationships in WordNet); a more accurate model may be possible.