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CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 5
Clicked: 5103
Using SVM to construct a Chinese dependency parser
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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",
volume="7",
number="2",
pages="199-203",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0199"
}
%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
TY - JOUR
T1 - Using SVM to construct a Chinese dependency parser
A1 - Xu Yun
A1 - Zhang Feng
J0 - Journal of Zhejiang University Science A
VL - 7
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SP - 199
EP - 203
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0199
Abstract: 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%.
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