Full Text:   <2359>

CLC number: TP391

On-line Access: 

Received: 2004-11-15

Revision Accepted: 2005-03-20

Crosschecked: 0000-00-00

Cited: 5

Clicked: 4774

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.2 P.199-203


Using SVM to construct a Chinese dependency parser

Author(s):  Xu Yun, Zhang Feng

Affiliation(s):  Computer Department, Beijing Institution of Technology, Beijing 100081, China

Corresponding email(s):   yxu1976@126.com, yxu1976@gmail.com

Key Words:  Syntactic parsing, Dependency, Support Vector Machine (SVM)

Xu Yun, Zhang Feng. Using SVM to construct a Chinese dependency parser[J]. Journal of Zhejiang University Science A, 2006, 7(2): 199-203.

@article{title="Using SVM to construct a Chinese dependency parser",
author="Xu Yun, Zhang Feng",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Using SVM to construct a Chinese dependency parser
%A Xu Yun
%A Zhang Feng
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 2
%P 199-203
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0199

T1 - Using SVM to construct a Chinese dependency parser
A1 - Xu Yun
A1 - Zhang Feng
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 2
SP - 199
EP - 203
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0199

In Chinese, dependency analysis has been shown to be a powerful syntactic parser because the order of phrases in a sentence is relatively free compared with English. Conventional dependency parsers require a number of sophisticated rules that have to be handcrafted by linguists, and are too cumbersome to maintain. To solve the problem, a parser using SVM (Support Vector Machine) is introduced. First, a new strategy of dependency analysis is proposed. Then some chosen feature types are used for learning and for creating the modification matrix using SVM. Finally, the dependency of phrases in the sentence is generated. Experiments conducted to analyze how each type of feature affects parsing accuracy, showed that the model can increase accuracy of the dependency parser by 9.2%.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Cortes, C., Vapnik, V.M., 1995. Support Vector Networks. Machine Learning, 20:273-297.

[2] Charniak, E., 1993. Statistical Language Learning. The MIT Press, Cambridge, p.116-121.

[3] Collins, M., 1996. A New Statistical Parser Based on Bigram Lexical Dependencies. Proceedings 34th Annual Meeting of Association for Computational Linguistics. University of California, Santa Cruz, CA, p.184-191.

[4] Guo, Y.H., Zhou, C.L., 2000. A study of the parsing strategy and the generative algorithm for dependency relation network of Chinese sentences. Journal of Zhejiang University (Engineering Science), 27:637-645 (in Chinese).

[5] Hirao, T., Isozaki, H., 2002. Extracting Important Sentences with Support Vector Machines. Proceedings of the 19th International Conference on Computational Linguistics. Taipei University, Taipei, p.342-348.

[6] Kudo, T., Matsumoto, Y., 2001. Chunking with Support Vector Machines. Proceedings of NAACL 2001. Carnegie Mellon University, Pittsburgh, PA, USA, p.192-199.

[7] Li, J.M., Zhang, B., Lin, F.Z., 2003. Training algorithms for Support Vector Machines. Journal of Tsinghua Univ. (Sci. & Tech.), 43:32-37 (in Chinese).

[8] Magerman, D., 1995. Statistical Decision Tree Model for Parsing. Proceedings 33rd Annual Meeting of Association for Computational Linguistics. Massachusetts, Cambridge, p.276-283.

[9] Zhang, Y.Q., Zhou, Q., 2002. Automatic identification of Chinese Base Phrase. Journal of Chinese Information Processing, 16:1-8 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE