基于图神经网络的文本分类方法研究综述OA北大核心CSTPCD

Text classification is an important task in the field of natural language processing,aiming to assign given text data to a predefined set of categories.Traditional text classification methods can only handle data in Euclidean space and cannot process non-Euclidean data such as graphs.For text data with graph structure,it is not directly processable and can-not capture the non-Euclidean structure in the graph.Therefore,how to apply graph neural networks to text classification tasks is one of the current research hotspots.This paper reviews the text classification methods based on graph neural net-works.Firstly,it outlines the traditional text classification methods based on machine learning and deep learning,and sum-marizes the background and principles of graph convolutional neural networks.Secondly,it elaborates on the text classifi-cation methods based on graph neural networks according to different types of graph networks,and conducts an in-depth analysis of the application of graph neural network models in text classification.Then,it compares the current text classifi-cation models based on graph neural networks through comparative experiments and discusses the classification perfor-mance of the models.Finally,it proposes future research directions to further promote the development of this field.