Vol.62, No.3, 2020, pp.1217-1231, doi:10.32604/cmc.2020.06787
Prison Term Prediction on Criminal Case Description with Deep Learning
  • Shang Li1, Hongli Zhang1, *, Lin Ye1, Shen Su2, Xiaoding Guo1, Haining Yu1, 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: zhanghongli@hit.edu.cn.
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.
Neural networks, prison term prediction, criminal case, text comprehension.
Cite This Article
Li, S., Zhang, H., Ye, L., Su, S., Guo, X. et al. (2020). Prison Term Prediction on Criminal Case Description with Deep Learning. CMC-Computers, Materials & Continua, 62(3), 1217–1231.
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