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We explore the application of memorybased 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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Deaf and hard-of-hearing (DHH) individuals have long struggled to be fully included, educationally, socially, and career-wise, in the mainstream of Moroccan society. Although the government has demonstrated philosophically that provision of education to children with disabilities K-12 is within their purview, they have yet to take substantive steps to effect this change. This chapter provides an overview of the state of education of DHH in Morocco and ongoing efforts to address challenges to full educational opportunities. More specifically, this chapter describes how a recent project funded by the United States Aid for International Development (USAID) has had a significant impact on education of the deaf there.