Textual Content Prediction via Fuzzy Attention Neural Network Model without Predefined Knowledge

作     者：Canghong Jin Guangjie Zhang Minghui Wu Shengli Zhou Taotao Fu Canghong Jin;Guangjie Zhang;Minghui Wu;Shengli Zhou;Taotao Fu

作者机构：School of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China College of Computer ScienceZhejiang UniversityHangzhou 310012China Zhejiang Police CollegeHangzhou 310000China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2020年第17卷第6期

页      面：211-222页

核心收录：

学科分类：0810[工学-信息与通信工程] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：support of the Science&Technology Development Project of Hangzhou Province,China(Grant No.20162013A08) the Research Project Support for Education of Zhejiang Province,China(Grant No.Y201941372) 

主　　题：judgment content understanding pre-trained model fuzzification content representation vectors 

摘      要：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 ***,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison *** researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external *** these methods require a lot of time and labor ***,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal *** propose a framework that combines value-based rules and a fuzzy text to predict the target prison *** procedure in our framework includes information extraction,term fuzzification,and document vector *** 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 *** results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared ***,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.