The purpose of Similar Case Matching(SCM)is to distinguish whether legal documents are similar,which is a specific application of text matching and is vital to the retrieval of similar cases.Compared with conventional texts,legal texts are typically longer,and SCM aims to realize matching for the same case.Moreover,the difference between case texts is negligible;therefore,calculating text similarity using previous text-matching methods is challenging.This study establishes a SCM model that integrates key elements of lending cases to address the issues of text matching in lending cases.To obtain richer semantic features from texts,regular expressions are constructed to obtain specific case elements of lending cases,such as the loan-delivery form and the basic attributes of borrowers,which are then combined with the original case text to jointly learn the semantic features of the legal text and key elements of the case.Additionally,pretrained models with shared weights are used to encode different instruments separately,and the outputs of specific encoding layers of the pretrained models are fused to obtain richer semantic information.Finally,the proposed model incorporates a supervised comparison learning framework to utilize the text information more effectively and further improve the performance of SCM.Experiments on the CAIL2019-SCM dataset show that this model improves the accuracy of the test set by 1.05 percentage points compared with LFESM models.
Similar Case Matching(SCM)Siamese networkcontrastive learningpretrained modelkey legal element
国家科技期刊平台
NSTL
万方数据
维普
