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	Graph-Based Dependency Parsing with Recursive Neural Network
	Huang, Pingping ; Chang, Baobao
	2015
关键词	Dependency parsing Recursive neural network Weighted-sum pooling
英文摘要	Graph-based dependency parsing models have achieved state-of-the-art performance, yet their defect in feature representation is obvious: these models enforce strong independence assumptions upon tree components, thus restricting themselves to local, shallow features with limited context information. Besides, they rely heavily on hand-crafted feature templates. In this paper, we extend recursive neural network into dependency parsing. This allows us to efficiently represent the whole sub-tree context and rich structural information for each node. We propose a heuristic search procedure for decoding. Our model can also be used in the reranking framework. With words and pos-tags as the only input features, it gains significant improvement over the baseline models, and shows advantages in capturing long distance dependencies.; EI; CPCI-S(ISTP); pinghpp@pku.edu.cn; chbb@pku.edu.cn; 227-239; 9427
语种	英语
出处	CHINESE COMPUTATIONAL LINGUISTICS AND NATURAL LANGUAGE PROCESSING BASED ON NATURALLY ANNOTATED BIG DATA (CCL 2015)
DOI标识	10.1007/978-3-319-25816-4_19
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436967]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Huang, Pingping,Chang, Baobao. Graph-Based Dependency Parsing with Recursive Neural Network. 2015-01-01.

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