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基于BERT的盗窃罪构成要件识别方法
引用本文:费志伟,艾中良,张可,曹禹.基于BERT的盗窃罪构成要件识别方法[J].计算机系统应用,2022,31(4):229-237.
作者姓名:费志伟  艾中良  张可  曹禹
作者单位:华北计算技术研究所,北京100083,中国司法大数据研究院,北京100043
基金项目:国家重点研发计划(2018YFC0832306, 2018YFC0831203, 2018YFC0831206)
摘    要:随着人工智能技术的发展,人工智能技术在生活中被广泛使用,并逐步深入到司法审理中.但在实际应用中存在着可解释性不足,不能有效的辅助审理这一问题.针对这一问题,本文结合刑事案件审理过程中依据犯罪构成采用的四要件理论,从犯罪构成的四要件角度,设计了构成要件识别任务.筛选了盗窃罪中一些构成要件,构建盗窃罪构成要件数据集.并基于...

关 键 词:四要件  盗窃罪  BERT  文本分类  深度学习  自然语言处理
收稿时间:2021/7/5 0:00:00
修稿时间:2021/7/30 0:00:00

Constitutive Elements Identification Method of Theft Crime Based on BERT
FEI Zhi-Wei,AI Zhong-Liang,ZHANG Ke,CAO Yu.Constitutive Elements Identification Method of Theft Crime Based on BERT[J].Computer Systems& Applications,2022,31(4):229-237.
Authors:FEI Zhi-Wei  AI Zhong-Liang  ZHANG Ke  CAO Yu
Affiliation:North China Institute of Computing Technology, Beijing 100083, China;China Justice Big Data Institute, Beijing 100043, China
Abstract:With the development of artificial intelligence technology, it has been widely used in life and gradually penetrated judicial proceedings. However, there is insufficient interpretability in practical applications and thus it cannot effectively assist trials. In light of the four-element theory used in criminal case trials according to the constitution of a crime, this paper addresses the above problem by designing an identification task of the four elements constituting a crime. Some constituent elements of crimes of theft are screened, and a data set of the constituent elements is constructed. Moreover, a constitutive elements identification model is developed on the basis of the pre-trained language model BERT and then tested on the data set constructed in this paper, with the identification accuracy reaching 93.54%. Constructing an auxiliary sentencing algorithm based on the constituent elements can improve the interpretability of the existing algorithm and more effectively assist judges in hearing cases.
Keywords:four elements  crime of theft  BERT  text classification  deep learning  natural language processing (NLP)
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