Combining generative and discriminative approaches to unsupervised dependency parsing via dual decomposition

	Combining generative and discriminative approaches to unsupervised dependency parsing via dual decomposition
	Jiang, Yong; Han, Wenjuan; Tu, Kewei
	2017
会议录名称	2017 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, EMNLP 2017
页码	1689-1694
发表状态	已发表
摘要	Unsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maximization algorithm and its variants, or learning discriminative models using the discriminative clustering algorithm. In this paper, we propose a new learning strategy that learns a generative model and a discriminative model jointly based on the dual decomposition method. Our method is simple and general, yet effective to capture the advantages of both models and improve their learning results. We tested our method on the UD treebank and achieved a state-of-the-art performance on thirty languages. © 2017 Association for Computational Linguistics.
会议地点	Copenhagen, Denmark
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	20194207538745
EI主题词	Clustering algorithms ; Image segmentation ; Maximum principle ; Natural language processing systems ; Syntactics
EI分类号	Data Processing and Image Processing:723.2 ; Information Sources and Analysis:903.1
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/29234
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. Combining generative and discriminative approaches to unsupervised dependency parsing via dual decomposition[C]:Association for Computational Linguistics (ACL),2017:1689-1694.