论文|Tian X, Fang Y, Weng Y, Luo Y, Cheng H, Wang Z*. K-Means Clustering for Controversial Issues Merging in Chinese Legal Texts

本文原载Legal Knowledge and Information Systems，由四川大学数学学院翁洋副教授、四川大学法学院王竹教授等科研人员创作，系四川大学“智慧法治”超前部署学科系列学术成果。后续会持续分享四川大学“智慧法治”超前部署学科系列学术成果，欢迎大家阅读。

Abstract: In the fact of growing number of cases, Chinese courts have gradually formed a trial mode to improve the efficiency of trials by conducting trials around the controversial issues. However, identifying the controversy issue in specific cases is not only affected by the uncertainty of facts and laws, but also by the discretion of the judges and extra-case factors, and cannot be expressed as a standard format, which lead to the controversial issues based case retrieval a challenge problem. In this paper, we propose a controversial issues merging algorithm based on K-means clustering for Chinese legal texts. The proposed algorithm can determine the number of clusters of the given cause of action automatically and merge the controversial issues semantically, which makes the case information retrieval more accurate and effective.

Keywords: information retrieval; K-means clustering; controversial issues

Tian X, Fang Y, Weng Y, Luo Y, Cheng H, Wang Z*. K-Means Clustering for Controversial Issues Merging in Chinese Legal Texts, Legal Knowledge and Information Systems, vol. 313, pp. 215-219, 2018. (EI)（论文下载）