Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges' expertise. By analyzing judges' trial logic, we find that...

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Autores principales:	Shaolin Ao, Yongbin Qin*, Yanping Chen, Ruizhang Huang
Formato:	article
Lenguaje:	EN
Publicado:	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2021
Materias:	intelligent case assignment neural networks text matching text representation Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7ebb4bcba6814a67897314bd72a4b5cd
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id	oai:doaj.org-article:7ebb4bcba6814a67897314bd72a4b5cd
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spelling	oai:doaj.org-article:7ebb4bcba6814a67897314bd72a4b5cd2021-11-07T00:38:54ZIntelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements1330-36511848-6339https://doaj.org/article/7ebb4bcba6814a67897314bd72a4b5cd2021-01-01T00:00:00Zhttps://hrcak.srce.hr/file/385036https://doaj.org/toc/1330-3651https://doaj.org/toc/1848-6339The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges' expertise. By analyzing judges' trial logic, we find that the order of criminal behaviors affects the final judgement. To solve these problems, we regard intelligent case assignment as a text-matching problem, and propose an intelligent case assignment method based on the chain of criminal behavior elements. This method introduces the chain of criminal behavior elements to enhance the structured semantic analysis of the case. We build a BCTA (Bert-Cnn-Transformer-Attention) model to achieve intelligent case assignment. This model integrates a judge's expertise in the judge's presentation, thus recommending the most compatible judge for the case. Comparing the traditional case assignment methods, our BCTA model obtains 84% absolutely considerable improvement under P@1. In addition, comparing other classic text matching models, our BCTA model achieves an absolute considerable improvement of 4% under P@1 and 9% under Macro F1. Experiments conducted on real-world data set demonstrate the superiority of our method.Shaolin AoYongbin Qin*Yanping ChenRuizhang HuangFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek articleintelligent case assignmentneural networkstext matchingtext representationEngineering (General). Civil engineering (General)TA1-2040ENTehnički Vjesnik, Vol 28, Iss 6, Pp 2138-2146 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intelligent case assignment neural networks text matching text representation Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	intelligent case assignment neural networks text matching text representation Engineering (General). Civil engineering (General) TA1-2040 Shaolin Ao Yongbin Qin* Yanping Chen Ruizhang Huang Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements
description	The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges' expertise. By analyzing judges' trial logic, we find that the order of criminal behaviors affects the final judgement. To solve these problems, we regard intelligent case assignment as a text-matching problem, and propose an intelligent case assignment method based on the chain of criminal behavior elements. This method introduces the chain of criminal behavior elements to enhance the structured semantic analysis of the case. We build a BCTA (Bert-Cnn-Transformer-Attention) model to achieve intelligent case assignment. This model integrates a judge's expertise in the judge's presentation, thus recommending the most compatible judge for the case. Comparing the traditional case assignment methods, our BCTA model obtains 84% absolutely considerable improvement under P@1. In addition, comparing other classic text matching models, our BCTA model achieves an absolute considerable improvement of 4% under P@1 and 9% under Macro F1. Experiments conducted on real-world data set demonstrate the superiority of our method.
format	article
author	Shaolin Ao Yongbin Qin* Yanping Chen Ruizhang Huang
author_facet	Shaolin Ao Yongbin Qin* Yanping Chen Ruizhang Huang
author_sort	Shaolin Ao
title	Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements
title_short	Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements
title_full	Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements
title_fullStr	Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements
title_full_unstemmed	Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements
title_sort	intelligent case assignment method based on the chain of criminal behavior elements
publisher	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
publishDate	2021
url	https://doaj.org/article/7ebb4bcba6814a67897314bd72a4b5cd
work_keys_str_mv	AT shaolinao intelligentcaseassignmentmethodbasedonthechainofcriminalbehaviorelements AT yongbinqin intelligentcaseassignmentmethodbasedonthechainofcriminalbehaviorelements AT yanpingchen intelligentcaseassignmentmethodbasedonthechainofcriminalbehaviorelements AT ruizhanghuang intelligentcaseassignmentmethodbasedonthechainofcriminalbehaviorelements
_version_	1718443619609739264

Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

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