@Article{cmc.2020.06787,
AUTHOR = {Shang Li, Hongli Zhang, Lin Ye, Shen Su, Xiaoding Guo, Haining Yu, Binxing Fang},
TITLE = {Prison Term Prediction on Criminal Case Description with Deep Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1217--1231},
URL = {http://www.techscience.com/cmc/v62n3/38350},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.06787}
}