Statistical Learning of Semitic Morphology Using Autosegmental Orthography

Abstract The root and pattern system, as well as the system of reduplication, are essential to the morphological analysis of Arabic words. (McCarthy 1979, 1981) Few computational morphology systems have been designed to parse concatenative morphology, as well as roots and reduplication simultaneously, without the help of a dictionary. By using simple statistics, we show an algorithm that can learn both the concatenative morphology as well as the roots and template. This paper shows an approach that is analogous to the the tier-based autosegmental approach developed by Goldsmith (1976), and applied to Semitic languages in McCarthy (1979).