A Possibilistic Approach for the Automatic Morphological Disambiguation of Arabic Texts

This paper presents a new approach for Arabic non-vocalized texts disambiguation based on a possibilistic classifier. A morphological analyzer provides all the possible solutions and the values of the morphological features of words. When texts are vocalized, the number of solutions is reduced and in many cases, we can identify the correct analysis of the input word. The main idea of this paper is to exploit this type of texts in order to learn contextual dependencies between the different values of morphological features modeled as a possibilistic network. This knowledge is used later to disambiguate non-vocalized texts. In order to evaluate our approach, we perform experiments on a corpus of arabic stories. In this paper, we present results concerning the Part-Of-Speech (POS) which is the main morphological feature. Our results are compared to the SVM-based system called MADA.