Improving Arabic Dependency Parsing with Lexical and Inflectional Morphological Features

We explore the contribution of different lexical and inflectional morphological features to dependency parsing of Arabic, a morphologically rich language. We experiment with all leading POS tagsets for Arabic, and introduce a few new sets. We show that training the parser using a simple regular expressive extension of an impoverished POS tagset with high prediction accuracy does better than using a highly informative POS tagset with only medium prediction accuracy, although the latter performs best on gold input. Using controlled experiments, we find that definiteness (or determiner presence), the so-called phi-features (person, number, gender), and undiacritzed lemma are most helpful for Arabic parsing on predicted input, while case and state are most helpful on gold.