Improving Arabic dependency parsing with form-based and functional morphological features

Abstract

We explore the contribution of morphological features - both lexical and inflectional - to dependency parsing of Arabic, a morphologically rich language. Using controlled experiments, we find that definiteness, person, number, gender, and the undiacritzed lemma are most helpful for parsing on automatically tagged input. We further contrast the contribution of form-based and functional features, and show that functional gender and number (e.g., "broken plurals") and the related rationality feature improve over form-based features. It is the first time functional morphological features are used for Arabic NLP. © 2011 Association for Computational Linguistics.