Abstract：Action-based dependency parsing, also known as deterministic dependency parsing, has often been regarded as an 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 action-based dependency parsers with complex parsing methods such as generative and discriminative parsers on the standard data set of Penn Chinese Treebank. The results show that, for Chinese dependency parsing, action-based parsers outperform generative and discriminative parsers. Furthermore, we propose two kinds of models for the modeling of parsing actions in action-based Chinese dependency parsing. We take the original action-based dependency parsers as baseline systems. The results show that our two models perform better than the baseline systems while maintaining the same time complexity, and our best result improves much over the baseline.
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2007, Vol. 21 
