Natural Language Understanding and Generation
LIU Quan, YU Zhengtao, GAO Shengxiang, HE Shizhu, LIU Kang
2022, 36(11): 140-147.
Simiar Case matching is an important task in intelligent justice, especially for case retrieval and same-case same-judgment. Owing to the long text and the subtle difference between legal documents, existing deep matching models are difficult to achieve ideal results. To address this issue, this paper proposes a method of integrating case elements to improve the matching of similar cases with a focus on the private lending cases. First, six types of private lending case elements are formulated and extracted by regular expressions, represented in the form of one -hot word vectors. Then the legal text is filtered and formed in reverse order, represented by BERT capture the long-distance dependence. The legal text representation and the case element representation is fused by the linear network and then encoded by BiLSTM for high-dimensional representation. Finally, the vector representation similarity matrix is constructed through the twin network framework, and the final similarity is decided by semantic interaction and vector pooling. The experimental results show that the proposed model is better than the baseline model on the CAIL2019-SCM public data set.