A Comparative Study on Different Types of Approaches to Bengali document Categorization
This is an incremental study applying existing methods to Bengali document categorization, which may benefit researchers in natural language processing for low-resource languages.
The study compared three supervised learning techniques (SVM, Naive Bayes, SGD) for Bengali document categorization across twelve categories, using feature selection methods like Chi square and normalized TFIDF to analyze their efficiency.
Document categorization is a technique where the category of a document is determined. In this paper three well-known supervised learning techniques which are Support Vector Machine(SVM), Naïve Bayes(NB) and Stochastic Gradient Descent(SGD) compared for Bengali document categorization. Besides classifier, classification also depends on how feature is selected from dataset. For analyzing those classifier performances on predicting a document against twelve categories several feature selection techniques are also applied in this article namely Chi square distribution, normalized TFIDF (term frequency-inverse document frequency) with word analyzer. So, we attempt to explore the efficiency of those three-classification algorithms by using two different feature selection techniques in this article.