Chinese Dependency Parsing Based on An Improved Model of MST

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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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  -