关键词: 自然语言处理, 多标签文本分类, 深度学习

Abstract:

As a basic task in natural language processing, text classification has been studied in the 1950s. Now the single-label text classification algorithm has matured, but there is still a lot of improvement on multi-label text classification. Firstly, the basic concepts and basic processes of multi-label text classification are introduced, including data set acquisition, text preprocessing, model training and prediction results. Secondly, the methods of multi-label text classification are introduced. These methods are mainly divided into two categories：traditional machine learning methods and the methods based on deep learning. Traditional machine learning methods mainly include problem transformation methods and algorithm adaptation methods. The methods based on deep learning use various neural network models to handle multi-label text classification problems. According to the model structure, they are divided into multi-label text classification methods based on CNN structure, RNN structure and Transfomer structure. The data sets commonly used in multi-label text classification are summarized. Finally, the future development trend is summarized and analyzed.

Key words: natural language processing, multi-label text classification, deep learning