Research on Chinese news text classification method based on deep neural network

Jie Wang

doi:10.1117/12.3014512

9 January 2024 Research on Chinese news text classification method based on deep neural network

Jie Wang

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296919 (2024) https://doi.org/10.1117/12.3014512
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

In the process of text classification, due to the short length of news headlines and sparse text features, it is difficult for headline samples to provide sufficient and valuable features for classification learning, resulting in low classification accuracy. This paper combines TextCNN with the GRU model, and uses the extraction algorithm proposed in this paper for word embedding, and proposes the TC-GRU (TextCNN-GRU) model. In order to verify the effectiveness of the model in this paper, experiments were carried out on THUCNews, Sogou News Dataset and Fudan University Text Dataset. Experimental results show that the accuracy of the TC-GRU model is generally higher than that of other comparative models

Citation Download Citation

Jie Wang "Research on Chinese news text classification method based on deep neural network", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296919 (9 January 2024); https://doi.org/10.1117/12.3014512

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available