Deep Graph Neural Networks for Text Classification Task

Abstract

Text classification is to organizing documents into predetermined categories, usually by machinery learn algorithms. It is a significant ways to organize and utilize the large amount of information that exists in unstructured text format. Text classification is an important module in text processing, and its applications are also very extensive, such as garbage filtering, news classification, part-of-speech tagging, and so on. With the continuous development of deep learning in recent years! Its applications are also very extensive, such as: garbage filtering, news classification, part-of-speech tagging, and so on. But the text also has its own characteristics. According to the characteristics of the text, the general process of text classification is: 1. Preprocessing; 2. Text representation and feature selection; 3. Construction of a classifier; 4. The task of text classification refers to classifying texts into only single or many types in TC system. Some researchers are beginning to apply deep neural networks to tasks such as the text classification we mentioned above. Although the research around the task has made great progress, the review of this task is very scarce, and there is a lack of a comprehensive review of the development of the task in recent years. Therefore, we present a survey of research in text classification to create taxonomies. Finally, it is by giving vital effects, the direction of future research, and those challenges that may counter in the research field.