Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 18, 08 December 2023
Open
Access | Article
Chinese news topic prediction using bidirectional encoder representation from transformers
* Author to whom correspondence should be addressed.
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.
Keywords
deep learning, natural language processing, BERT, Chinese news topic prediction
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-201-5
- ISBN (Online)
- 978-1-83558-202-2
- Published Date
- 08 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/18/20230358
- Copyright
- 08 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited