Probabilistic models for action-based chinese dependency parsing

Abstract

Action-based dependency parsing, also known as deterministic dependency parsing, has often been regarded as a time efficient parsing algorithm while its parsing accuracy is a little lower than the best results reported by more complex parsing models. In this paper, we compare actionbased dependency parsers with complex parsing methods such as all-pairs parsers on Penn Chinese Treebank. For Chinese dependency parsing, actionbased parsers outperform all-pairs parsers. But action-based parsers do not compute the probability of the whole dependency tree. They only determine parsing actions stepwisely by a trained classifier. To globally model parsing actions of all steps that are taken on the input sentence, we propose two kinds of probabilistic parsing action models that can compute the probability of the whole dependency tree. Results show that our probabilistic parsing action models perform better than the original action-based parsers, and our best result improves much over them. © Springer-Verlag Berlin Heidelberg 2007.