Deep Learning Based Text Classification Methods

Abstract

Text classification tasks are indispensable in natural language processing. With the development of Internet technology, the way people transmit information has changed from letters to the Internet. With the increase in the amount of information, manual data annotation is inefficient. After 2010, the emergence of deep learning methods has brought text classification into an epoch-making stage. ReNN-> MLP-> RNN-> CNN-> Attention -> Transformer-> GNN and other text classification methods are gradually being developed and known by everyone, which also shows that text classification is developing towards a text feature that does not rely on manually acquired text features and is directly learning from the text content and modelling. This paper will start with basic knowledge, first let everyone understand the nature, application and historical background of text classification, will briefly introduce shallow learning, then enter deep learning, Select the classic model from the six classification methods from ReNN to Transformer for brief analysis, briefly analyse the model from the principle of the model, what problems it is good at solving, problems or shortcomings that it is not good at solving. Finally, the paper describes the performance of these models on the dataset.