A Neural-Network-Based Model of Charge Prediction via the Judicial Interpretation of Crimes

The neural-network-based charge prediction, which is to predict the defendants' charges from the criminal case documents via neural network, has been a development-potential affair in artificial intelligence (AI) based legal assistant system and made some achievements. Neural network is playing important role to capture deep information in current work. However, charge prediction suffers from serious data imbalance in real-world situation. Only high-frequency charges are easy to be predicted whereas plenty of low-frequency ones are hard to be hold. Furthermore, the presence of confusing charges makes prediction worse. Here, we propose a novel model of charge prediction via the judicial interpretation of crimes (CPJIC) to provide more accurate charge prediction. The concept of crime interpretation is introduced into CPJIC, which alleviates the problems resulted from data imbalance and confusing charges. With the technique of embedding, both fact description and crime interpretation are embedded into a low-dimensional vector space as well as a neural network, delivering implemented computable charge prediction. The experimental results demonstrate that CPJIC can identify the low-frequency and confusing charges better than previous work.