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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (2): 96-104    DOI: 10.11925/infotech.2096-3467.2017.0990
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An Unsupervised Approach to Optimize Chinese Word Segmentation on Domain Literature
Weijian Ni,Haohao Sun,Tong Liu(),Qingtian Zeng
College of Computer Science and Technology, Shandong University of Science and Technology, Qingdao 266510, China
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Abstract  

[Objective] This paper aims to improve the performance of Chinese word segmentation techniques on domain literature by optimizing results of existing approaches. [Methods] First, we proposed a new criteria of Term Frequency Deviation (TFD) to capture word formation characteristics of domain literature based on the analysis of segmentation errors. Then, we developed an unsupervised segmentation refining approach with the help of TFD. [Results] We examined the proposed approach with agriculture documents. It improved the segmentation results of three popular Chinese word segmentation approaches (i.e., ICTCLAS, THULAC and LTP) by 2%~3% in F1 measure. The proposed approach was easy to use and robustness to parameters. [Limitations] The recall of the proposed approach needs to be improved. [Conclusions] The new Chinese word segmentation approach, which imrpoves the performance of traditional methods on domain literature, could be applied to other fields due to its independence of domain-specific vocabulary and annotated corpus.

Key wordsDomain Literature      Chinese Word Segmentation      Segmentation Refining      Term Frequency Deviation     
Received: 28 September 2017      Published: 07 March 2018

Cite this article:

Weijian Ni,Haohao Sun,Tong Liu,Qingtian Zeng. An Unsupervised Approach to Optimize Chinese Word Segmentation on Domain Literature. Data Analysis and Knowledge Discovery, 2018, 2(2): 96-104.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0990     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I2/96

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