基于卷积神经网络的辅助分案方法研究

敖绍林; 秦永彬; 黄瑞章; 陈艳平; 刘丽娟; 郑庆华; 陈昌恒; 程少芬

doi:10.11959/j.issn.2096-0271.2022020

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基于卷积神经网络的辅助分案方法研究

研究 | 更新时间：2024-06-03

- 基于卷积神经网络的辅助分案方法研究
- Research on auxiliary division method based on convolutional neural network
- 大数据 2022年8卷第2期页码：145-157
- 作者机构：
  
  1. 贵州大学计算机科学与技术学院，贵州贵阳 550025
  2. 公共大数据国家重点实验室，贵州贵阳 550025
  3. 贵州师范学院，贵州贵阳 550025
  4. 西安交通大学计算机科学与技术学院，陕西西安 710049
  5. 贵州省高级人民法院，贵州贵阳 550081
- 作者简介：
  
  [ "敖绍林（1995- ），男，贵州大学计算机科学与技术学院硕士生，主要研究方向为自然语言处理、文本分析" ]
  [ "秦永彬（1980- ），男，博士，贵州大学计算机科学与技术学院教授，主要研究方向为大数据处理、云计算、文本挖掘" ]
  [ "黄瑞章（1979- ），女，博士，贵州大学计算机科学与技术学院副教授，主要研究方向为信息检索、文本挖掘" ]
  [ "陈艳平（1980- ），男，博士，贵州大学计算机科学与技术学院副教授，主要研究方向为人工智能、自然语言处理" ]
  [ "刘丽娟（1980- ），女，贵州师范学院讲师，主要研究方向为法学与思想政治教育" ]
  [ "郑庆华（1969- ），男，博士，西安交通大学计算机科学与技术学院教授，主要研究方向为多媒体远程教育、计算机网络安全" ]
  [ "陈昌恒（1978- ），男，贵州省高级人民法院信息技术处处长、三级调研员，主要研究方向为司法审判应用" ]
  [ "程少芬（1982- ），女，贵州省高级人民法院信息技术处应用推广科科长、四级调研员，主要研究方向为司法审判应用" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U1836205);国家自然科学基金资助项目;The National Natural Science Foundation of China(91746116);国家自然科学基金资助项目;The National Natural Science Foundation of China(62066007);国家自然科学基金资助项目;The National Natural Science Foundation of China(62066008);国家自然科学基金资助项目;The National Natural Science Foundation of China(62166007);贵州省科技重大专项计划基金资助项目;Special Science and Technology Projects of Guizhou Province([2017]3002);贵州省科学技术基金重点项目;The Key Projects of Science and Technology of Guizhou Province([2020]1Z055);贵州省研究生科研基金立项课题;Project of Guizhou Province Graduate Research Fund(YJSCXJH[2019]102)
- DOI：10.11959/j.issn.2096-0271.2022020
  中图分类号： TP183
- 网络首发：2022-03，
  
  纸质出版：2022-03-15
- 稿件说明：
移动端阅览
敖绍林, 秦永彬, 黄瑞章, 等. 基于卷积神经网络的辅助分案方法研究[J]. 大数据, 2022,8(2):145-157.

Shaolin AO, Yongbin QIN, Ruizhang HUANG, et al. Research on auxiliary division method based on convolutional neural network[J]. Big data research, 2022, 8(2): 145-157.
敖绍林, 秦永彬, 黄瑞章, 等. 基于卷积神经网络的辅助分案方法研究[J]. 大数据, 2022,8(2):145-157. DOI： 10.11959/j.issn.2096-0271.2022020.

Shaolin AO, Yongbin QIN, Ruizhang HUANG, et al. Research on auxiliary division method based on convolutional neural network[J]. Big data research, 2022, 8(2): 145-157. DOI： 10.11959/j.issn.2096-0271.2022020.

摘要

法院系统中主要有人工指定分案和简单随机分案两种模式。这两种模式无法做到人案的自动匹配，存在金钱案、关系案等弊端。目前分案方法的相关研究主要存在法官表示和案件匹配两个难点。结合法官历史审判数据，在法官表示中融合法官擅长的审判领域，提出一种融合审判质量的法官表示方法。然后，通过卷积神经网络学习案件表示和法官表示中不同粒度的抽象语义特征向量，计算案件和多个法官的特征向量间的余弦相似度，用向量相似度表示案件与法官的匹配度，输出匹配值较高的前N个法官作为案件的推荐法官。在贵州省某法院真实数据下进行实验，结果表明该方法推荐法官的正确率比传统方法高80%。

Abstract

The court system mainly has two modes: manual designated division and simple random division.The above method cannot achieve automatic matching of persons and cases

and there are drawbacks such as money cases and relationship cases.At present

the research on division method mainly has two difficulties: judge’s representation and case matching.Combining the judge’s historical trial data

the judge’s expertise in the judge’s representation was integrated

and a judge representation method that integrates the quality of the trial was proposed.Then

the abstract semantic feature vectors of different granularities in the case representation and the judge representation were learned through the convolutional neural network

the cosine similarity between the case and the feature vectors of multiple judges was calculated

and vector similarity was used to indicate the matching degree between the case and the judge

the top N judges with high matching value were output as recommended judges for the case.Experiments with real data from a court in Guizhou Province

and the results show that the accuracy of the method for recommending judges is 80% higher than the traditional method.

关键词

Keywords

references

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