A Semi-Automatic Annotation Method of Effect Clue Words for Chinese Patents Based on Co-Training

Na Deng, Chunzhi Wang, Mingwu Zhang, Zhiwei Ye, Liang Xiao, Jingbai Tian, Desheng Li and Xu Chen
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Na Deng: Hubei University of Technology, Wuhan, China
Chunzhi Wang: Hubei University of Technology, Wuhan, China
Mingwu Zhang: Hubei University of Technology, Wuhan, China
Zhiwei Ye: Hubei University of Technology, Wuhan, China
Liang Xiao: Hubei University of Technology, Wuhan, China
Jingbai Tian: Hubei University of Technology, Wuhan, China
Desheng Li: Hubei University of Technology, Wuhan, China
Xu Chen: Zhongnan University of Economics and Law, Wuhan, China

International Journal of Data Warehousing and Mining (IJDWM), 2018, vol. 14, issue 4, 1-19

Abstract: In the era of big data, the latest and most advanced technologies are usually revealed to the world in the form of patents. Patents include abundant technical, economic and legal information. A deep analysis and mining of patents can provide important support for enterprises. Patent effect annotation is an important step in patent analysis and mining, and the extraction of patent effect clue words can greatly improve the accuracy and recall rate of annotation. This article summarizes the classification and characteristics of effect clue words, and proposes a co-training-based method of extracting effect clue words from Chinese patents suitable for various fields. Through a strategy called self-filtering, this method can gradually enrich effect clue words thesaurus by iterations, not relying on any other third-party filters. The experiments give the detailed steps, comparisons and boosting of the method.

Date: 2018
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