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Towards an Optimal Solution to Lemmatization in Arabic

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Abstract

Lemmatization—computing the canonical forms of words in running text—is an important component in any NLP system and a key preprocessing step for most applications that rely on natural language understanding. In the case of Arabic, lemmatization is a complex task because of the rich morphology, agglutinative aspects, and lexical ambiguity due to the absence of short vowels in writing. In this presentation, we introduce a new lemmatizer tool that combines a machine-learning-based approach with a lemmatization dictionary, the latter providing increased accuracy, robustness, and flexibility to the former.
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