Supervised Text classification is a supervised technique that uses labeled training data to learn the classification system and then automatically classifies the remaining text using the learned system. In this paper, we are going to explore the potential of Hidden Markov Models in the Arabic texts classification. Through experimental results, we can notice that our method based-HMM reached a high classification on a large corpus of Arabic news articles. Moreover, we show that feature selection and the addition of trigrams have a great impact on the classification accuracy.
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