Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

Chinese Dependency Parsing Based on An Improved Model of MST

Authors
Qi Sun, Yang Xiang, Xiao Tu
Corresponding Author
Qi Sun
Available Online September 2016.
DOI
10.2991/icence-16.2016.3How to use a DOI?
Keywords
Dependency parsing; Maximum spanning tree; Condition random fields
Abstract

In this paper, a Chinese dependency parsing method was presented based on improved Maximum Spanning Tree algorithm. Within this method, Conditional Random Field (CRF) is adopted to establish sequence labeling model. Recognizing POS of head node is employed to modify the weights of directed edges in the MST model. Comparative experiments on CoNLL 2009 data set show that the new method shows better performance than current Chinese dependency methods, with precision reaching to 85.45%.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
Series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-229-9
ISSN
2352-538X
DOI
10.2991/icence-16.2016.3How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Qi Sun
AU  - Yang Xiang
AU  - Xiao Tu
PY  - 2016/09
DA  - 2016/09
TI  - Chinese Dependency Parsing Based on An Improved Model of MST
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 9
EP  - 14
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.3
DO  - 10.2991/icence-16.2016.3
ID  - Sun2016/09
ER  -