Research on auxiliary division method based on convolutional neural network

Shaolin AO; Yongbin QIN; Ruizhang HUANG; Yanping CHEN; Lijuan LIU; Qinghua ZHENG; Changheng CHEN; Shaofen CHENG

doi:10.11959/j.issn.2096-0271.2022020

您当前的位置：

首页 >

文章列表页 >

Research on auxiliary division method based on convolutional neural network

STUDA | 更新时间：2024-06-03

- Research on auxiliary division method based on convolutional neural network
- Big data research Vol. 8, Issue 2, Pages: 145-157(2022)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2022020
  CLC： TP183
- Online First：2022-03，
  
  Published：15 March 2022
- 稿件说明：
移动端阅览
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：

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

秦永彬 , 冯丽 , 陈艳平 , 等 . “智慧法院”数据融合分析与集成应用 [J ] . 大数据 , 2019 , 5 ( 3 ): 35 - 46 .

QIN Y B , FENG L , CHEN Y P , et al . “Intelligent Court” data fusion analysis and integrated application [J ] . Big Data Research , 2019 , 5 ( 3 ): 35 - 46 .

SALTON G , BUCKLEY C . Termweighting approaches in automatic text retrieval [J ] . Information Processing ＆Management , 1988 , 24 ( 5 ): 513 - 523 .

DEERWESTER S , DUMAIS S T , FURNAS G W , et al . Indexing by latent semantic analysis [J ] . Journal of the American Society for Information Science , 1990 , 41 ( 6 ): 391 - 407 .

BLEI D M , NG A Y , JORDAN M I . Latent Dirichlet allocation [J ] . Journal of Machine Learning Research , 2001 , 3 : 601 - 608 .

BENGIO Y , DUCHARME R , VINCENT P , et al . A neural probabilistic language model [J ] . Journal of Machine Learning Research , 2003 , 3 : 1137 - 1155 .

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv preprint,2013,arXiv:1301.3781 .

MIKOLOV T , SUTSKEVER I , CHEN K , et al . Distributed representations of words and phrases and their compositionality [C ] // Proceedings of the 26th International Conference in Neural Information Processing Systems.Red Hook:Curran Associates Inc . , 2013 : 3111 - 3119 .

PENNINGTON J , SOCHER R , MANNING C . Glove:global vectors for word representation [C ] // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2014 : 1532 - 1543 .

BOJANOWSKI P , GRAVE E , JOULIN A , et al . Enriching word vectors with subword information [J ] . Transactions of the Association for Computational Linguistics , 2017 , 5 : 135 - 146 .

PETERS M E , NEUMANN M , IYYER M , et al . Deep contextualized word representations [J ] . arXiv preprint,2018,arXiv:1802.05365 .

DEVLIN J , CHANG M W , LEE K , et al . BERT:pre-training of deep bidirectional transformers for language understanding [J ] . arXiv preprint,2018,arXiv:1810.04805 .

KIM Y . Convolutional neural networks for sentence classification [J ] . arXiv preprint,2014,arXiv:1408.5882 .

冯兴杰 , 张乐 , 曾云泽 . 基于多注意力CNN的问题相似度计算模型 [J ] . 计算机工程 , 2019 , 45 ( 9 ): 284 - 290 .

FENG X J , ZHANG L , ZENG Y Z . Question similarity calculation model based on multiattention CNN [J ] . Computer Engineering , 2019 , 45 ( 9 ): 284 - 290 .

CHIU J P C , NICHOLS E . Named entity recognition with bidirectional LSTMCNNs [J ] . Transactions of the Association for Computational Linguistics , 2016 , 4 : 357 - 370 .

孙国荣 . 智能分案:兼顾公平与效率 [J ] . 人民法治 , 2018 ( 2 ): 49 - 51 .

SUN G R . Smart division:taking into account fairness and efficiency [J ] . People·Rule of Law , 2018 ( 2 ): 49 - 51 .

王崇璋 . 法院案件分配标准体系实证研究:以HB市中基层法院为例 [D ] . 淮北:淮北师范大学 , 2018 .

WANG C Z . An empirical study on the standard system of court case allocation-take the example of the HB municipal intermediate and basic courts [D ] . Huaibei:Huaibei Normal University , 2018 .

王建华 , 张宁 . 美国联邦法院民事案件的立案规则 [N ] . 人民法院报 , 2015 - 10 - 23 (8).

WANG J H , ZHANG N . Rules for filing civil cases in the U.S.Federal Court [N ] . People’s Court Daily , 2015 - 10 - 23 (8).

兰世民 , 兰馨 , 缪新森 . 法院分案若干问题研究 [J ] . 法律适用 , 2012 ( 6 ): 95 - 100 .

LAN S M , LAN X , MIAO X S . Research on several Issues of court division [J ] . Journal of Law Application , 2012 ( 6 ): 95 - 100 .

贾炜 , 常鹏 , 石鹏 . 一种实现法院分案的方法及装置:CN107665212A [P ] . 2018 - 02 - 06 .

JIA W , CHANG P , SHI P . Method and device for case allocation of court:CN107665212A [P ] . 2018 - 02 - 06 .

顾钊铨 , 方滨兴 , 韩伟红 , 等 . 一种法院案件智能化分案辅助方法及系统:CN109872052A [P ] . 2019 - 06 - 11 .

GU Z Q , FANG B X , HAN W H , et al . Court case intelligent case division auxiliary method and system:CN109872052A [P ] . 2019 - 06 - 11 .

陈芳序 . 法官工作量均等视角下法院随机分案系统的检视与重构:以A法院刑事分案为切入点 [J ] . 海峡法学 , 2019 , 21 ( 2 ): 87 - 95 .

CHEN F X . Inspection and reconstruction of the court random division system from perspective of equal workload for judges-based on criminal division of court A [J ] . Cross Strait Legal Science , 2019 , 21 ( 2 ): 87 - 95 .

王小新 . 法院分案系统的检视与重构:以X法院刑事案件分配为例 [J ] . 法律适用 , 2016 ( 4 ): 112 - 116 .

WANG X X . Review and reconstruction of the court division system:taking X court criminal case distribution as an example [J ] . Journal of Law Application , 2016 ( 4 ): 112 - 116 .

孙晓东 . 中国司法评估制度完善研究 [J ] . 广东社会科学 , 2018 ( 6 ): 231 - 242 .

SUN X D . An analysis on improvement of judicial performance evaluation system of China [J ] . Social Sciences in Guangdong , 2018 ( 6 ): 231 - 242 .

LAN Z Z , CHEN M D , GOODMAN S , et al . ALBERT:a lite BERT for selfsupervised learning of language representations [J ] . arXiv preprint,2019,arXiv:1909.11942 .

Views

430

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

State of the art and future perspectives of the applications of deep learning in the medical image analysis

Related Author

Lihui WANG

Yongbin QIN

Related Institution

Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273162 Visits Today：68

⁰