JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (1): 40-47.doi: 10.6040/j.issn.1671-9352.1.2021.048

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A similarity case matching method combining segment encoding and affine-mechanism

LAI Hua1,2, ZHANG Heng-tao1,2, XIAN Yan-tuan1,2*, HUANG Yu-xin1,2   

  1. 1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
    2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China
  • Published:2023-02-12

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Abstract: Similarity case matching(SCM)task is to judge whether the cases described in two judgment documents are similar. SCM is usually regarded as the text matching problem of judgment documents and has important applications in the judicial trial. Existing deep learning models mostly encode long texts of cases into a single vector, and it is difficult for the model to learn the subtle differences between the cases from long texts. Considering that the content of each part of the case text is relatively fixed, this paper proposes to split the long case text into multiple pieces and encode them separately to obtain the subtle features of different parts. At the same time, learnable affine-transformation is used to improve the similarity scoring module, so that the model learn more subtle differences, which further improves the performance of case matching. The experimental results on the CAIL2019-SCM data set show that compared to another model, the accuracy of the method proposed in this paper have increased by 1.89%.

Key words: similarity case matching, text matching, legal intelligence, convolution, affine transformation

CLC Number: 

  • TP391
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