ARABIC PART-OF-SPEECH TAGGING USING THE SENTENCE STRUCTURE

This paper presents a system for Arabic Part-Of-Speech Tagging, which combines morphological analysis with Hidden Markov Model (HMM) and relies on the Arabic sentence structure. On the one hand, the morphological analysis is used to reduce the size of the tags lexicon by segmenting Arabic words in their prefixes, stems, and suffixes due to the fact that Arabic is a derivational language. On the other hand, HMM is used to represent the Arabic sentence structure in order to take into account the logical linguistic sequencing. For these purposes, an appropriate tagging system has been proposed to represent the main Arabic part of speech in a hierarchical manner allowing an easy expansion whenever it is needed. Each tag in this system is used to represent a possible state of the HMM and the transitions between tags (states) are governed by the syntax of the sentence. A corpus of some old texts, extracted from Books of third century (Hijri), is manually tagged using our developed tagset. and then used for training and testing this system. First experiments conducted on the dataset give a recognition rate of 96 % and thus are very promising compared to the data size tagged till now and used in the training