WordNet: a lexical database for English
Communications of the ACM
The use of bigrams to enhance text categorization
Information Processing and Management: an International Journal
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms
Journal of Intelligent Information Systems
Classifying functional relations in factotum via WordNet hypernym associations
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Fully Automatic Text Categorization by Exploiting WordNet
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
A semantic term weighting scheme for text categorization
Expert Systems with Applications: An International Journal
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Supervised text classification is the task of automatically assigning a category label to a previously unlabeled text document. We 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 Word-Net expansion (i.e., incorporating words defined by various relationships in WordNet), a more accurate model may be possible. In this paper, we report preliminary results obtained from a comprehensive study where WordNet features, part of speech tags, and term weighting schemes are incorporated into two-category text classification models generated by both a Naive Bayes text classifier and an SVM text classifier. We characterize the behaviour of these classifiers on fifteen document collections extracted from the Reuters-21578, USENET, DigiTrad, and 20-Newsgroups text corpora. Experimental results show that incorporating WordNet features, utilizing part of speech tags during WordNet expansion, and term weighting schemes have no positive effect on the accuracy of the Naive Bayes and SVM classifiers.