Word sense disambiguation for arabic text categorization

In this paper, we present two contributions for Arabic Word Sense Disambiguation. In the first one, we propose to use both two external resources AWN and WN based on Term to Term Machine Translation System (MTS). The second contribution relates to the disambiguation strategies, it consists of choosing the nearest concept for the ambiguous terms, based on more relationships with different concepts in the same local context. To evaluate the accuracy of our proposed method, several experiments have been conducted using Feature Selection methods; Chi-Square and CHIR, and two Machine Learning techniques; the Naïve Bayesian (NB) and Support Vector Machine (SVM). The obtained results illustrate that using the proposed method increases greatly the performance of our Arabic Text Categorization System.