Law Article Prediction Based on Deep Learning

Abstract

Analysing judicial documents is a significant task in the field of legal intelligence, of which the prediction of law articles can help ordinary people better comprehend basic legal knowledge. It can be seen as a multi-label classification problem. Our work uses description of the cases as the input and views law articles as labels. After word segmentation and feature extraction, the classifier model TextCNN is used to train and predict the results. Results of comparative experiments show that our approach can achieve competitive results.