Research on Chinese short text classification based on ERNIE-TEXTCNN model

Mengtao Wang; Junwu Xu

doi:10.1117/12.3005031

9 October 2023 Research on Chinese short text classification based on ERNIE-TEXTCNN model

Mengtao Wang, Junwu Xu

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127911B (2023) https://doi.org/10.1117/12.3005031
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

The development of the Internet has led to a sharp increase in the amount of Chinese text data, and the classification of short Chinese texts has problems such as fewer words, more ambiguity, sparse features, and irregular features, which leads to poor results of traditional text classification techniques. This paper designs and implements the ERNIE-TEXTCNN model. The model uses ERNIE as the word vector model, TEXTCNN extracts features from the input text vector, and softmax obtains the final classification result. Through comparative experiments with other methods, it is shown that the ERNIE-TEXTCNN model can extract text feature information very well, and has a good classification effect of Chinese short texts.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengtao Wang and Junwu Xu "Research on Chinese short text classification based on ERNIE-TEXTCNN model", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127911B (9 October 2023); https://doi.org/10.1117/12.3005031

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