Parsing modern standard Arabic using Treebank resources

A Treebank is a linguistic resource that is composed of a large collection of manually annotated and verified syntactically analyzed sentences. Statistical Natural Language Processing (NLP) approaches have been successful in using these annotations for developing basic NLP tasks such as tokenization, diacritization, part-of-speech tagging, parsing, among others. In this paper, we address the problem of exploiting Treebank resources for statistical parsing of Modern Standard Arabic (MSA) sentences. Statistical parsing is significant for NLP tasks that use parsed text as an input such as Information Retrieval, and Machine Translation. We conducted an experiment on Pen Arabic Treebank (PATB) and the parsing performance obtained in terms of Precision, Recall, and F-measure was 82.4%, 86.6%, 84.4%, respectively.