We present an approach to using a morphological analyzer for tokenizing and morphologically tagging (including partof-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We obtain accuracy rates on all tasks in the high nineties.
REF use SVM-classifiers for individual morphological features and a simple combining scheme for choosing among competing analyses proposed by the dictionary.
Arabic Tokenization, Part-Of-Speech Tagging And Morphological Disambiguation In One Fell Swoop
Annual Meeting Of The Association For Computational Linguistics
Computer Science