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| Fact-based similar case retrieval methods based on statutory knowledge |
Linrui LI1( ),Dongsheng WANG2,*(),Hongjie FAN2 |
1. Guanghua Law School, Zhejiang University, Hangzhou 310008, China
2. School of Information Management for Law, China University of Political Science and Law, Beijing 102249, China |
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Abstract Existing research on the retrieval task of similar cases ignores the legal logic that the model should imply, and cannot adapt to the requirements of case similarity criteria in practical applications. Few datasets in Chinese for case retrieval tasks are difficult to meet the research needs. A similar case retrieval model was proposed based on legal logic and strong interpretability, and a case event logic graph was constructed based on predicate verbs. The statutory knowledge corresponding to various crimes was integrated into the proposed model, and the extracted elements were input to a neural network-based scorer to realize the task of case retrieval accurately and efficiently. A Confusing-LeCaRD dataset was built for the case retrieval task with a confusing group of charges as the main retrieval causes. Experiments show that the normalized discounted cumulative gain of the proposed model on the LeCaRD dataset and Confusing-LeCaRD dataset was 90.95% and 94.64%, and the model was superior to TF-IDF, BM25 and BERT-PLI in all indicators.
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Received: 21 June 2023
Published: 01 July 2024
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| Fund: 长沙市科技重大专项项目（kh2202006）；中国政法大学科研创新项目（24KYGH013）；中央高校基本科研业务费专项资金. |
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Corresponding Authors:
Dongsheng WANG
E-mail: lilinrui1412@163.com;wangdsh@cupl.edu.cn
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基于法条知识的事理型类案检索方法
现有类案检索研究忽略了模型应当蕴含的法律逻辑，无法适应实际应用中案件相似标准的要求；类案检索任务的中文数据集较少，难以满足研究需求现状. 为此提出基于法律逻辑、有较强可解释性的类案检索模型，构建以谓语动词为基础的案件事理图谱. 将各类罪名对应的法条知识融入所提模型，将提取的不同要素输入以神经网络为基础的评分器以实现准确、高效的类案检索. 构建针对类案检索任务、以易混淆罪名组为主要检索案由的Confusing-LeCaRD数据集，所提模型在LeCaRD数据集和Confusing-LeCaRD数据集上的归一化折损累计增益分别为90.95%和94.64%，在各项指标上均优于TF-IDF、BM25和BERT-PLI模型.
关键词：
类案检索,
法条知识,
法律逻辑,
事理图谱,
深度学习
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