时间：2021-10-21
本文Joint Extraction Methods for Semantic Retrieval in Chinese Judicial Cases 原载2021 16th International Conference on Computer Science & Education (ICCSE)，由四川大学王竹教授等科研人员创作，系四川大学市场经济法治研究所系列学术成果。后续会持续分享四川大学市场经济法治研究所系列学术成果，欢迎大家阅读。
Abstract:The Supreme People’s Court of China has implemented the compulsory retrieval system for similar cases, making similar case retrieval the focus of research. Over 110 million judgment documents have been published on China Judgements Online, which provides massive data support. Recent approaches for semantic search are to embed queries and documents into semantic space and compute the similarity between them for ranking. These methods only represent the overall meaning of queries and documents, but learn a lot of redundant information from long text. In order to capture the case facts precisely from judgment documents, we propose a joint extraction model to build knowledge graphs for judicial cases. Experiments on recent judgment documents demonstrated the effectiveness of this knowledge graph construction process. The proposed case retrieval method can improve the efficiency for judicial trials, promote judicial justice, prevent the abuse of discretion, and unify the scale of adjudication.
Keywords: {Semantic search;Computational modeling;Education;case retrieval;knowledge graph;joint extraction;BERT}
H. Wu, S. Gu, Z. Wang and Y. Weng, "Joint Extraction Methods for Semantic Retrieval in Chinese Judicial Cases," 2021 16th International Conference on Computer Science & Education (ICCSE), Lancaster, United Kingdom, 2021, pp. 1126-1129.（论文下载）
