基于卷积神经网络的辅助分案方法研究

doi:10.11959/j.issn.2096-0271.2022020

Abstract

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.

Key words: text representation, convolutional neural network, smart division, smart court

CLC Number:

TP183

Shaolin AO, Yongbin QIN, Ruizhang HUANG, Yanping CHEN, Lijuan LIU, Qinghua ZHENG, Changheng CHEN, Shaofen CHENG. Research on auxiliary division method based on convolutional neural network[J]. Big Data Research, 2022, 8(2): 145-157.

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References 24

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数据集类别	案件数/个	三元组数/个
训练集	1 238	21 046
验证集	152	2 584
测试集	156	2 652

N	正确率
1	86.54%
3	96.15%
5	98.07%
7	98.72%

分案方法	正确率
分案方法	Top1	Top3	Top5	Top7
均衡分案	5.85%	18.72%	30.77%	43.07%
摇号分案	5.40%	18.02%	27.27%	35.85%
本文方法	86.54%	96.15%	98.07%	98.72%

分案方法	正确率
分案方法	Top1	Top3	Top5	Top7
TF-IDF+cosine	1.28%	24.36%	57.05%	66.03%
BM25+cosine	16.67%	43.59%	66.67%	90.38%
本文方法	86.54%	96.15%	98.07%	98.72%

分案方法	正确率
分案方法	Top1	Top3	Top5	Top7
Tri-BiLSTM	49.36%	75.64%	88.46%	96.15%
Tri-BERT	78.21%	91.67%	96.15%	97.44%
Tri-ALBERT	76.92%	94.23%	97.44%	98.72%
本文方法	86.54%	96.15%	98.07%	98.72%

Research on auxiliary division method based on convolutional neural network

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参数ε	正确率
参数ε	Top1	Top3	Top5	Top7
0.1	79.49%	94.23%	97.44%	98.08%
0.5	82.69%	92.95%	97.44%	99.36%
1.0	86.54%	96.15%	98.07%	98.72%

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