Prison Term Prediction of Judicial Cases Based on Hierarchical Attentive Recurrent Neural Network

doi:10.12068/j.issn.1005-3026.2022.03.006

Abstract

Abstract: In order to solve the problem of poor accuracy of prison term prediction， a prison term prediction model was proposed on the basis of multi-channel hierarchical attentive recurrent neural network. The model improves the traditional recurrent neural network， introduces BERT word embedding， multichannel mode and hierarchical attention mechanism， and transforms the prison term prediction task into text classification problem. The model uses hierarchical bidirectional recurrent neural network to model the legal case text， and captures the importance of different words and sentences at word level and sentence level through hierarchical attention mechanism. Finally， a multi-channel embedding vector that effectively represents the case text is generated. The experimental results show that the proposed model has higher prediction performance compared with the existing prison term prediction model based on deep learning.

Key words: prison term prediction; hierarchical attention mechanism; bidirectional gated recurrent unit; multi-channel; text classification

CLC Number:

TP183

LI Da-peng， ZHAO Qi-hui， XING Tie-jun， ZHAO Da-zhe. Prison Term Prediction of Judicial Cases Based on Hierarchical Attentive Recurrent Neural Network[J]. Journal of Northeastern University(Natural Science), 2022, 43(3): 344-349.

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