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Two-channel hierarchical attention mechanism model for short text classification

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Abstract

Text classification plays an important role in information science. In order to address the issues of low classification efficiency, low accuracy, and incomplete text feature extraction in existing classification methods, this work offers a two-channel hierarchical attention mechanism short text classification model (TCHAM). First, a layered word vector attention mechanism is developed to improve the capture of keywords and phrases. Second, the TextBERT model is applied to train the word vector representation to solve the problem of multiple meanings of a word. Third, a two-channel neural network is utilized to achieve parallel acceleration. Finally, the output information of the two-channel neural network is fused to raise the accuracy of news text classification. The experimental results show that under the same environment and dataset, TCHAM increases the accuracy of text classification, reaching 98.03\(\%\) for the THUCNews dataset and 95.65\(\%\) for the SogouNews dataset, and its classification performance outperforms the comparison model.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://thuctc.thunlp.org/.

  2. https://drive.google.com/uc?export=download &id=0Bz8a_Dbh9QhbUkVqNEszd0pHaFE.

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Author notes

  1. Shiyang Hu and Haihui Huang have contributed equally to this study.

Authors and Affiliations

  1. School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Guanghui Chang, Shiyang Hu & Haihui Huang

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  1. Guanghui Chang
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  2. Shiyang Hu
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Chang, G., Hu, S. & Huang, H. Two-channel hierarchical attention mechanism model for short text classification. J Supercomput 79, 6991–7013 (2023). https://doi.org/10.1007/s11227-022-04950-1

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