A Novel Context-aware Similar Case Matching and Recommendation Method

Abstract

As far as we know, it is usually difficult for the accuracy of similar cases obtained by the existing case recommendation methods to meet the needs of judges, and thus the effect of auxiliary judgment is limited. Therefore, in this paper a novel context-aware similar case matching and recommendation model (CASCMR). In order to achieve end-to-end efficient text matching and recommendation was proposed. The model uses a multi-semantic document expression framework to realize the pre-calculation and storage of text vectors, so as to reduce the matching time and improve the efficiency. Specifically, in order to model the long legal text, CASCMR uses BERT for encoding since its attention mechanism can capture the long-term dependency well. At the same time, the global and local information of the legal text as captured by Bi-LSTM and CNN, respectively, is considered to be helpful to improve the representation of the text, as well as the prediction performance of the model. The proposed model was then applied to the similar case matching task of CAIL2019-SCM, and its accuracy is higher than that of the state-of-the-art method.

Keywords

• similar case matching
• similar case recommendation
• bert
• attention mechanism
• law ai