Comparison of methods for integrating lexicon information in Chinese word segmentation

Feng Xue
School of Computer, Beijing Information Science & Technology University, Beijing 100192, China

Abstract

Currently the mainstream methods for Chinese word segmentation exploit statistical machine learning models. These methods usually require manual-annotated segmented sentences as training corpus, yet have neglected the annotated large-scale lexicon resources which have been built before, where these resources can be highly valuable when cross-domain evaluation is conducted, as the gold-standard sentence-level annotations arerare. Recently, the integration of lexicon formation into word segmentation models has gained increasing interest. As a whole, the integration methods can be classified into two categories: one being based on character-based models that cast word segmentation problem as sequence labeling, and the other being based on word-basedmodels that use beam-search to decode. This paper compared these two models, and combined them. Experimental results on benchmark datasetsshow that lexicon information can be more fully explored after combination, and finally the combined model can achieve better performances with both in-and cross-domain settings.

Foundation Support

北京市教委科技计划面上项目(KM201411232012)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.05.0643
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 1
Section: Survey
Pages: 8-10,17
Serial Number: 1001-3695(2019)01-002-0008-03

Publish History

[2019-01-05] Printed Article

Cite This Article

冯雪. 中文分词模型词典融入方法比较 [J]. 计算机应用研究, 2019, 36 (1): 8-10,17. (Feng Xue. Comparison of methods for integrating lexicon information in Chinese word segmentation [J]. Application Research of Computers, 2019, 36 (1): 8-10,17. )

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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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