Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking

We investigate the tasks of general morpho-logical tagging, diacritization, and lemmatiza-tion for Arabic. We show that for all tasks we consider, both modeling the lexeme explicitly, and retuning the weights of individual classi-fiers for the specific task, improve the perfor-mance. 1 Previous Work Arabic is a morphologically rich language: in our training corpus of about 288,000 words we find 3279 distinct morphological tags, with up to 100,000 pos-sible tags.1 Because of the large number of tags, it is clear that morphological tagging cannot be con-strued as a simple classification task. Hajic ̌ (2000