基于BERT与改进BP神经网络的盗窃案刑期预测方法研究

为了应对智慧法院项目中刑期预测任务的实际需求,提出了基于BERT与改进BP神经网络的刑期预测模型。以盗窃案为切入点,剖析相关案情要素,介绍刑期预测的整体框架和具体过程。基于大量真实案件数据,结合法官的审理流程,首先使用BERT识别裁判文书中的案情要素,然后基于规则抽取对应的涉案金额,最后使用改进的BP神经网络预测刑期,并与传统模型对比。实验证明,提出的模型刑期预测的平均误差小于2.5个月,优于进行对比的传统模型。

Research on Prediction Model of Sentence for Theft based on BERT and Improved BP Neural Network

In order to meet the actual needs of sentence prediction task in the smart court project,this paper proposes a sentence prediction model based on BERT(Bidirectional Encoder Representation from Transformers)and improved BP neural network.Starting the theft cases,relevant case elements are analyzed,and the overall framework and specific process of sentence prediction are introduced.Based on a large amount of real case data and the judge's trial process,BERT is used to identify the case elements in the judgment documents.Then the amount of money involved based on the rules is extracted.Finally,the improved BP neural network is used to predict the sentence period and the proposed model is compared with the traditional one.Experiments show that the average error of the proposed sentence prediction model is less than 2.5 months,which is better than the traditional model used for comparison.