Memory-Based Morphological Analysis Generation and Part-of-Speech Tagging of Arabic

We explore the application of memory-based learning to morphological analysis and part-of-speech tagging of written Arabic, based on data from the Arabic Treebank. Morphological analysis -- the construction of all possible analyses of isolated unvoweled wordforms -- is performed as a letter-by-letter operation prediction task, where the operation encodes segmentation, part-of-speech, character changes, and vocalization. Part-of-speech tagging is carried out by a bi-modular tagger that has a subtagger for known words and one for unknown words. We report on the performance of the morphological analyzer and part-of-speech tagger. We observe that the tagger, which has an accuracy of 91.9% on new data, can be used to select the appropriate morphological analysis of words in context at a precision of 64.0 and a recall of 89.7.

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