Improving Arabic Part-of-Speech Tagging through Morphological Analysis

This paper describes our newly-developed second order hidden Markov model part-of-speech tagging system specially designed to tag Arabic texts using small training data. The tagger achieves encouraging results. In addition, the paper also presents a hybrid tagging architecture for Arabic, in which our tagger augmented with a weighted morphological analyzer. Finally, we compare the tagger results both standalone and utilizing a highly coverage morphological analyzer. Experimental results are presented and discussed using small training corpus. The experiments show that the best proposed hybrid architecture significantly improves unknown words POS tagging accuracy. 96.6% precision rates are obtained when unknown words occur in the test set.

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