Chinese news topic prediction using bidirectional encoder representation from transformers

Abstract

Nowadays, there are many researches on natural language processing (NLP). Through the research of NLP method, many problems in machine learning field have been solved. However, since the study of Chinese NLP has not developed rapidly until recent years, there is still much to be studied on Chinese NLP. As an excellent pre-training model, whether Bidirectional Encoder Representation from Transformers (BERT) performs well on specific Chinese NLP remains to be studied. Therefore, this paper uses BERT for Chinese NLP, and trains BERT model by collecting news title data to achieve Chinese text classification. Finally, the prediction results are studied by statistical methods. The research shows that BERT method performs well on Chinese NLP and can predict different types of news headlines well. Although it performs differently on different kinds of titles, its performance is satisfactory on the whole, and the prediction results are relatively balanced in different categories. Therefore, BERT can be used as a very practical and efficient NLP method. At the same time, it can also be predicted that it will play a great role in Chinese NLP.