Unsupervised neural dependency parsing
2016
Source Publication2016 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, EMNLP 2016
Pages763-771
Status已发表
Abstract

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

Conference PlaceAustin, TX, United states
Indexed ByEI
Language英语
Funding ProjectNational Natural Science Foundation of China[61503248]
PublisherAssociation for Computational Linguistics (ACL)
EI Accession Number20194107499800
EI KeywordsNatural language processing systems
EI Classification NumberComputer Programming Languages:723.1.1 ; Data Processing and Image Processing:723.2
Original Document TypeConference article (CA)
Document Type会议论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29539
Collection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
Affiliation
School of Information Science and Technology, ShanghaiTech University, Shanghai, China
First Author AffilicationSchool of Information Science and Technology
First Signature AffilicationSchool of Information Science and Technology
Recommended Citation
GB/T 7714
Jiang, Yong,Han, Wenjuan,Tu, Kewei. Unsupervised neural dependency parsing[C]:Association for Computational Linguistics (ACL),2016:763-771.
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