On the parameter space of generative lexicalized statistical parsing models
On the parameter space of generative lexicalized statistical parsing models
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A fast, accurate deterministic parser for Chinese
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A classifier-based parser with linear run-time complexity
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
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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 deterministic 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, deterministic parsers outperform generative and discriminative parsers. Furthermore, based on the observation that deterministic parsing algorithms are greedy algorithms which choose the most probable parsing action at every step, we propose three kinds of ungreedy deterministic dependency parsing algorithms to globally model parsing actions. Results show that ungreedy deterministic dependency parsers perform better than original deterministic dependency parsers while maintaining the same time complexity, and our best parser improves much over all other parsers.