A model fro recommendation of applicable law articles based on transfer learning

Abstract:

In this paper, we propose a model for the recommendation of applicable law articles. As an application, the judgment documents data sets of fraud and divorce dispute are selected from the criminal and civil cases. Based on transfer learning, the legal word vectors are trained from the pre-trained general word vectors by using the FastText model. Then, the text is classified according to the well trained vectors. The simulation results show that for both the fraud and the divorce dispute, after the transfer learning, the applicable law can be recommended comprehensively and accurately for the case discription text, especially for the targeted regulations and judicial interpretations. With the continuous improvement of the transfer learning mode, our model is expected to be further applied to the evidence pushing and sentencing prediction.