Matching Similar Cases with Legal Knowledge Fusion
Zheng Jie1,Huang Hui2,Qin Yongbin2()
1Department of Information Science, Guiyang Vocational and Technical College, Guiyang 550081, China 2College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
[Objective] This paper constructs a model to match similar cases with integrated legal knowledge, aiming to improve the accuracy of case matching. [Methods] First, we concatenated the legal knowledge with the case texts, which helped the model learn characteristics of legal knowledge and text information simultaneously. Then, we used the LSTM network to model text segmentally, and increased the length of the accommodated texts. Finally, we used triplet loss and adversarial-based contrastive loss to jointly train the model and enhanced its robustness. [Results] The proposed model significantly improved the accuracy of similar case matching, which is 7.07% higher than the baseline BERT model. [Limitations] We used longer text sequences for matching, which is more time consuming than other models. [Conclusions] The proposed model has stronger matching and generalization ability, which helps legal case retrieval.
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