Supervised learning Methods for Bangla Web Document Categorization
This addresses the lack of studies on Bangla text categorization, which is an incremental application of existing methods to a new language domain.
This paper tackled the problem of automatically categorizing Bangla web documents using four supervised learning methods (Decision Tree, KNN, Naïve Bayes, and SVM), finding that all methods performed satisfactorily with SVM achieving good results for high-dimensional and noisy data.
This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5), K-Nearest Neighbour (KNN), Naïve Bays (NB), and Support Vector Machine (SVM) for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.