Text classification of agricultural news based on ERNIE+BiLSTM+CNN

Jing Wu; Lin Zhang

doi:10.1117/12.3031138

19 July 2024 Text classification of agricultural news based on ERNIE+BiLSTM+CNN

Jing Wu, Lin Zhang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131811H (2024) https://doi.org/10.1117/12.3031138
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In the era of sustainable intelligent agriculture, agricultural news appears to be intermingled with diverse, sparse, lack of datasets. In this context, the article proposes a text categorization model of ERNIE-BiLSTM-CNN incorporating an attention mechanism. The model adopts ERNIE as a language model, applies BiLSTM to extract the long-term dependent features of the text, adjusts its bias degree by introducing the attention mechanism, and finally uses CNN to extract the local features of the text. Experimental results demonstrate that the ERNIE-BiLSTM-CNN model is better for text classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jing Wu and Lin Zhang "Text classification of agricultural news based on ERNIE+BiLSTM+CNN", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131811H (19 July 2024); https://doi.org/10.1117/12.3031138

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