Research PaperNo Access

Temporal and Semantic Fusion for Multi-Label Crime Classification via a TCN-BERT-Coupled Approach

Party School of the CPC Hubei Provincial Committee, (Hubei Provincial Institute of Public Administration), Wuhan, Hubei 430078, P. R. China

E-mail Address: siska101@126.com

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Teng Chen

https://orcid.org/0009-0000-3051-3577

School of Computer Science, China University of Geosciences, Wuhan, Hubei 430078, P. R. China

E-mail Address: teng_chen2019@163.com

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, and

Yuewei Wang

https://orcid.org/0000-0003-3360-5842

School of Computer Science, China University of Geosciences, Wuhan, Hubei 430078, P. R. China

E-mail Address: yuewei.w@cug.edu.cn

Corresponding author.

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https://doi.org/10.1142/S0218126624501500Cited by:0 (Source: Crossref)

Abstract

Artificial Intelligence (AI) techniques leverage the justice system in terms of effectiveness and efficiency. AI-empowered multi-label crime classification can facilitate the precise and expedient categorization of various legal documents. Multi-label classification within the justice system has an indispensable role in achieving accurate legal categorization that invigorates case analysis, optimizes resource distribution and refines the contours of legal processes. To support this critical function, this paper proposes a temporal convolutional network (TCN)-bidirectional encoder representations from transformers (BERT)-coupled model for multi-label crime classification. The proposed method fuses the temporal formation and semantic information in the model to obtain a high-quality result. The experimental results show that the proposed method achieved the best accuracy in comparison to existing methods on a public dataset.

This paper was recommended by Regional Editor Tongquan Wei.

Keywords:

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