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	An effective neural network model for graph-based dependency parsing
	Pei, Wenzhe ; Ge, Tao ; Chang, Baobao
	2015
英文摘要	Most existing graph-based parsing models rely on millions of hand-crafted features, which limits their generalization ability and slows down the parsing speed. In this paper, we propose a general and effective Neural Network model for graph-based dependency parsing. Our model can automatically learn high-order feature combinations using only atomic features by exploiting a novel activation function tanhcube. Moreover, we propose a simple yet effective way to utilize phrase-level information that is expensive to use in conventional graph-based parsers. Experiments on the English Penn Treebank show that parsers based on our model perform better than conventional graph-based parsers. ? 2015 Association for Computational Linguistics.; EI; 313-322; 1
语种	英语
出处	53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/423629]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Pei, Wenzhe,Ge, Tao,Chang, Baobao. An effective neural network model for graph-based dependency parsing. 2015-01-01.

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