Research on multi-label classification of Chinese judicial texts based on BERT-TextCNN

Yawen Zhang; Xiaohui Li; Pingli Gu; Ke Zhang

doi:10.1117/12.2660132

3 February 2023 Research on multi-label classification of Chinese judicial texts based on BERT-TextCNN

Yawen Zhang, Xiaohui Li, Pingli Gu, Ke Zhang

Author Affiliations +

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125110E (2023) https://doi.org/10.1117/12.2660132
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Multi label classification of judicial text data is a hot issue in judicial artificial intelligence. However, judicial data has the characteristics of strong professionalism and long text. The performance of multi label classification task of judicial text using BERT model is poor. Aiming at the above problems, this paper proposes a BERT-TextCNN model, which uses BERT to extract text vectors, and introduces TextCNN to construct multi label classifiers for training, so as to extract semantic information features at different levels of abstraction and improve classification precision. In addition, the input character limit of BERT model will affect the classification results, so this paper rebuild the data set to make the data set meet the maximum input limit of BERT. This paper is tested on the multi label classification data set in the " China legal research Cup " in 2021. The experimental results show that compared with the BERT model, the proposed method has significantly improved the performance and can effectively improve the effect of multi label classification of Chinese judicial texts.

Citation Download Citation

Yawen Zhang, Xiaohui Li, Pingli Gu, and Ke Zhang "Research on multi-label classification of Chinese judicial texts based on BERT-TextCNN", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125110E (3 February 2023); https://doi.org/10.1117/12.2660132

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