Open Access

ARTICLE

Prison Term Prediction on Criminal Case Description with Deep Learning

Shang Li¹, Hongli Zhang^{1, *}, Lin Ye¹, Shen Su², Xiaoding Guo¹, Haining Yu^{1, 3}, Binxing Fang¹

1 School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
2 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.
3 Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong.

* Corresponding Author: Hongli Zhang, Email: .

Computers, Materials & Continua 2020, 62(3), 1217-1231. https://doi.org/10.32604/cmc.2020.06787

Abstract

The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal case. Recent advances in deep learning frameworks inspire us to propose a two-step method to address this problem. To obtain a better understanding and more specific representation of the legal texts, we summarize a judgment model according to relevant law articles and then apply it in the extraction of case feature from judgment documents. By formalizing prison term prediction as a regression problem, we adopt the linear regression model and the neural network model to train the prison term predictor. In experiments, we construct a realworld dataset of theft case judgment documents. Experimental results demonstrate that our method can effectively extract judgment-specific case features from textual fact descriptions. The best performance of the proposed predictor is obtained with a mean absolute error of 3.2087 months, and the accuracy of 72.54% and 90.01% at the error upper bounds of three and six months, respectively.

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Citations