Content Based Text Classification Using Morkov Models

Khalid Hussain Zargar, Manzoor Ahmad Chachoo

Abstract: Text categorization is the task of assigning predefined category to a set of documents. Several different models like SVM, Na?ve Bayes, KNN have been used in the past. In this paper we present another approach to automatically assign a category to a document. Our approach is based on the use of Markov Models. We consider text as bag of words and use Hidden Markov Model to assign the most appropriate catagory to the text. The proposed approach is based on the fact that while creating documents the user uses the specific vocabulary related to the particular category. Hidden Morkov models have been widely used in automatic speech recognition, part of speech tagging, information extraction but has not been used extensively for text categorization.

Keywords: Text Classification, Information gain, HMM, Text Processing, Viterbi Algorithim, Precision, Recall.