Unsupervised neural dependency parsing

	Unsupervised neural dependency parsing
	Jiang, Yong; Han, Wenjuan; Tu, Kewei
	2016
会议录名称	2016 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, EMNLP 2016
页码	763-771
发表状态	已发表
摘要	Unsupervised dependency parsing aims to learn a dependency grammar from text annotated with only POS tags. Various features and inductive biases are often used to incorporate prior knowledge into learning. One useful type of prior information is that there exist correlations between the parameters of grammar rules involving different POS tags. Previous work employed manually designed features or special prior distributions to encode such information. In this paper, we propose a novel approach to unsupervised dependency parsing that uses a neural model to predict grammar rule probabilities based on distributed representation of POS tags. The distributed representation is automatically learned from data and captures the correlations between POS tags. Our experiments show that our approach outperforms previous approaches utilizing POS correlations and is competitive with recent state-of-the-art approaches on nine different languages. © 2016 Association for Computational Linguistics
会议地点	Austin, TX, United states
收录类别	EI
资助项目	National Natural Science Foundation of China[61503248]
出版者	Association for Computational Linguistics (ACL)
EI入藏号	20194107499800
EI主题词	Natural language processing systems
EI分类号	Computer Programming Languages:723.1.1 ; Data Processing and Image Processing:723.2
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29539
专题	信息科学与技术学院_博士生信息科学与技术学院_PI研究组_屠可伟组
作者单位	School of Information Science and Technology, ShanghaiTech University, Shanghai, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Jiang, Yong,Han, Wenjuan,Tu, Kewei. Unsupervised neural dependency parsing[C]:Association for Computational Linguistics (ACL),2016:763-771.