A new and efficient stemming technique for Arabic Text Categorization

Text preprocessing of Arabic Language is a challenge and crucial stage in Text Categorization (TC) particularly and Text Mining (TM) generally. Stemming algorithms can be used in Arabic text preprocessing to reduce multiple forms of the word to one form (root or stem). Arabic stemming algorithms can be classified, according to the desired level of analysis, as root-based approach (exp Khoja); stem-based approach (Larkey); and statistical approach (n-garm). Yet no a complete stemmer for this language is available: The existing stemmers not have a high performance. In this paper, in order to improve the accuracy of stemming and therefore the accuracy of our proposed TC system, an efficient hybrid method is proposed for stemming Arabic text. The effectiveness of the aforementioned four methods was evaluated and compared in term of the accuracy of the Naïve Bayesian classifier used in our TC system. The proposed stemming algorithm was found to supersede the other stemming ones: The obtained results illustrate that using the proposed stemmer enhances the performance of Arabic Text Categorization: the averages accuracies are: 74.41% for khoja, 59.71% for light stemming, 48.17% for n-grams, and 82.33% for our stemmer.