基于异构数据联合训练的中文分词法

doi:10.16180/j.cnki.issn1007-7820.2019.04.007

Abstract

Abstract:

Chinese word segmentation technology is one of the key basic technologies in Chinese information processing. The Chinese word segmentation method based on deep learning model is widely concerned. However, the deep learning model requires large-scale data training to obtain good performance, but the current Chinese sub-word data is relatively lacking and the standards are not the same. This paper proposes a simple and effective method of heterogeneous data processing. Firstly, two artificially-set identifiers are added to different corpus data, and then the processed data is applied to the joint training of Bi-LSTM-CRF Chinese word segmentation model. Experimental results show that the Bi-LSTM-CRF model based on heterogeneous data joint training has better segmentation performance than the single data training model.

Key words: Chinese word segmentation, deep learning, Bi-LSTM-CRF, heterogeneous data, joint training, corpus

CLC Number:

TP391

JIANG Meng,WANG Ziniu,GAO Jianling. Chinese Word Segmentation Based on Joint Training of Heterogeneous Data[J].Electronic Science and Technology, 2019, 32(4): 29-33.

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References 16

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语料库	标识符
PKU	<pku>江泽民发表新年贺词</pku>
MSR	<msr>穆守家两间低矮的小屋里</msr>
AS	<as>民族所所长庄英章先生</as>
CityU	<cityu>选举出现了戏剧性的结果</cityu>

模型	PKU	MSR	AS	CityU
Bakeoff-best	0.950	0.964	-	-
(Zheng, 2013)	0.961	0.974	-	-
(Chen, 2017)	0.943	0.960	-	0.948
(Cai, 2017)	0.958	0.971	0.953	0.956
Bi-LSTM-CRF+多语料	0.960	0.974	0.954	0.962

模型	PKU	MSR	AS	CityU
Bi-LSTM-CRF	0.952	0.973	0.949	0.951
+多语料	0.953	0.973	0.950	0.953
+多语料+标签	0.960	0.974	0.954	0.962

Chinese Word Segmentation Based on Joint Training of Heterogeneous Data

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