Classifiers combination to arabic morphosyntactic disambiguation

Parts of speech tagging forms the important pre-processing step in many of the natural language processing applications like text summarization, question answering and information retrieval system. MorphoSyntactic disambiguation (part of speech tagging) is the process of classifying every word in a given context to its appropriate part of speech. In this paper, we first review all the supervised machine learning approaches that have been used in the part of speech tagging. Then we review all the Arabic works to compare and to confirm our need to develop an accurate and efficient Arabic morphosyntactic disambiguation system. Finally we propose a classifiers combination experimental framework for Arabic part of speech tagger in which three diverse probabilistic classifiers (hidden Markov, maximum entropy and transformation based learning) are combined using many different combination strategies to exploit their advantages.