A Comparative Study on Supervised and Unsupervised Learning Approaches for Multilingual Text Categorization

Recently users of internationally distributed information networks need tools and methods that enable them to discover, retrieve and categorize relevant information, in whatever language and form it may have been stored. This drives a convergence of numerous interests from diverse research communities focusing on the issues related to multilingual text categorization. In this work we compare and evaluate the performance of the leading supervised and unsupervised approaches for multilingual text categorization by using various performance measures and standard document corpora. For simplicity, we selected support vector machines (SVM) and latent semantic indexing (LSI) techniques as representatives of supervised and unsupervised methods for multilingual text categorization, respectively. The preliminary results show that our platform models including both supervised and unsupervised learning methods have the potentials for multilingual text categorization