Textual content prediction via fuzzy attention neural network model without predefined knowledge

Abstract

Text analysis is a popular technique for finding the most significant information from texts including semantic, emotional, and other hidden features, which became a research hotspot in the last few years. Specially, there are some text analysis tasks with judgment reports, such as analyzing the criminal process and predicting prison terms. Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge. All these methods require a lot of time and labor costs. Therefore, in this paper, we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge. We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term. The procedure in our framework includes information extraction, term fuzzification, and document vector regression. We carry out experiments with real-world judgment reports and compare our model's performance with those of ten traditional classification and regression models and two deep learning models. The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics. Finally, we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.