TitleAccelerating Graph-Based Dependency Parsing with Lock-Free Parallel Perceptron
AuthorsMa, Shuming
Sun, Xu
Zhang, Yi
Wei, Bingzhen
AffiliationMOE Key Lab of Computational Linguistics, School of EECS, Peking University, Beijing, China
Issue Date2018
Publisher7th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2018
Citation7th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2018. 2018, 11108 LNAI, 260-268.
AbstractDependency parsing is an important NLP task. A popular approach for dependency parsing is structured perceptron. Still, graph-based dependency parsing has the time complexity of O(n3), and it suffers from slow training. To deal with this problem, we propose a parallel algorithm called parallel perceptron. The parallel algorithm can make full use of a multi-core computer which saves a lot of training time. Based on experiments we observe that dependency parsing with parallel perceptron can achieve 8-fold faster training speed than traditional structured perceptron methods when using 10 threads, and with no loss at all in accuracy. © 2018, Springer Nature Switzerland AG.
URIhttp://hdl.handle.net/20.500.11897/530700
ISSN9783319994949
DOI10.1007/978-3-319-99495-6_22
IndexedEI
Appears in Collections:信息科学技术学院
计算语言学教育部重点实验室

Files in This Work
There are no files associated with this item.

Web of Science®



Checked on Last Week

Scopus®



Checked on Current Time

百度学术™



Checked on Current Time

Google Scholar™





License: See PKU IR operational policies.