Multi-label text classification using multinomial models

Abstract

Traditional approaches to pattern recognition tasks normally consider only the unilabel classification problem, that is, each observation (both in the training and test sets) has one unique class label associated to it. Yet in many real-world tasks this is only a rough approximation, as one sample can be labeled with a set of classes and thus techniques for the more general multi-label problem have to be explored. In this paper we review the techniques presented in our previous work and discuss its application to the field of text classification, using the multinomial (Naive Bayes) classifier. Results are presented on the Reuters-21578 dataset, and our proposed approach obtains satisfying results.