Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

Abstract

The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges' expertise. By analyzing judges' trial logic, we find that the order of criminal behaviors affects the final judgement. To solve these problems, we regard intelligent case assignment as a text-matching problem, and propose an intelligent case assignment method based on the chain of criminal behavior elements. This method introduces the chain of criminal behavior elements to enhance the structured semantic analysis of the case. We build a BCTA (Bert-Cnn-Transformer-Attention) model to achieve intelligent case assignment. This model integrates a judge's expertise in the judge's presentation, thus recommending the most compatible judge for the case. Comparing the traditional case assignment methods, our BCTA model obtains 84% absolutely considerable improvement under P@1. In addition, comparing other classic text matching models, our BCTA model achieves an absolute considerable improvement of 4% under P@1 and 9% under Macro F1. Experiments conducted on real-world data set demonstrate the superiority of our method.

Keywords

• intelligent case assignment
• neural networks
• text matching
• text representation