Predicting associated statutes for legal problems
作者：
Highlights：
• In the legal domain, this method for statute prediction is a new research topic.
• We predict relevant statutes for the problem described by everyday vocabulary.
• The gap between lay terms and legal terms was remedied without using a synopsis.
• Employing the Normalized Google Distance, SVM and Apriori algorithms into TPP.
• The result shows the performance of TPP is accurately and effectively.
摘要
•In the legal domain, this method for statute prediction is a new research topic.•We predict relevant statutes for the problem described by everyday vocabulary.•The gap between lay terms and legal terms was remedied without using a synopsis.•Employing the Normalized Google Distance, SVM and Apriori algorithms into TPP.•The result shows the performance of TPP is accurately and effectively.
论文关键词：Text mining,Statute,Criminal judgment,Normalized Google Distance (NGD),Support vector machines (SVM),Apriori algorithm
论文评审过程：Received 16 October 2013, Revised 29 May 2014, Accepted 9 July 2014, Available online 10 August 2014.
论文官网地址：https://doi.org/10.1016/j.ipm.2014.07.003
