ASMA: A System for Automatic Segmentation and Morpho-Syntactic Disambiguation of Modern Standard Arabic
Muhammad Abdul-Mageed, Mona Diab, Sandra Kübler · 2013
In this paper, we present ASMA, a fast and efficient system for automatic seg-mentation and fine grained part of speech (POS) tagging of Modern Standard Ara-bic (MSA). ASMA performs segmenta-tion both of agglutinative and of inflec-tional morphological boundaries within a word. In this work, we compare ASMA to two state of the art suites of MSA tools: AMIRA 2.1 (Diab et al., 2007; Diab, 2009) and MADA+TOKAN 3.2. (Habash et al., 2009). ASMA achieves comparable results to these two systems ’ state-of-the-art performance. ASMA yields an accu-racy of 98.34 % for segmentation, and an accuracy of 96.26 % for POS tagging with a rich tagset and 97.59 % accuracy with an extremely reduced tagset. 1