Character-Level Dependency Model for Joint Word Segmentation, POS Tagging, and Dependency Parsing in Chinese

Zhen GUO  Yujie ZHANG  Chen SU  Jinan XU  Hitoshi ISAHARA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.1   pp.257-264
Publication Date: 2016/01/01
Publicized: 2015/10/06
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7118
Type of Manuscript: PAPER
Category: Natural Language Processing
Keyword: 
joint model,  Chinese word segmentation and POS tagging,  dependency parsing,  word internal dependency structure,  semi-supervised learning,  

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Summary: 
Recent work on joint word segmentation, POS (Part Of Speech) tagging, and dependency parsing in Chinese has two key problems: the first is that word segmentation based on character and dependency parsing based on word were not combined well in the transition-based framework, and the second is that the joint model suffers from the insufficiency of annotated corpus. In order to resolve the first problem, we propose to transform the traditional word-based dependency tree into character-based dependency tree by using the internal structure of words and then propose a novel character-level joint model for the three tasks. In order to resolve the second problem, we propose a novel semi-supervised joint model for exploiting n-gram feature and dependency subtree feature from partially-annotated corpus. Experimental results on the Chinese Treebank show that our joint model achieved 98.31%, 94.84% and 81.71% for Chinese word segmentation, POS tagging, and dependency parsing, respectively. Our model outperforms the pipeline model of the three tasks by 0.92%, 1.77% and 3.95%, respectively. Particularly, the F1 value of word segmentation and POS tagging achieved the best result compared with those reported until now.