Text classification method based on improved long short term memory network

Jie Zhang; You Li; Huanhuan Li; Pan Gao; Dajian Deng; Jianwei Hao

doi:10.1117/12.3026902

27 March 2024 Text classification method based on improved long short term memory network

Jie Zhang, You Li, Huanhuan Li, Pan Gao, Dajian Deng, Jianwei Hao

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310528 (2024) https://doi.org/10.1117/12.3026902
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

With the booming development of the internet industry and the entry of big data into people's lives, it is particularly important to quickly extract people's needs from massive text information. This article proposes a text classification method based on an improved long and short term memory network. To address the problem of being unable to encode information from back to front, the Bi-LSTM model is improved to concatenate forward and backward outputs with the earliest word vectors to obtain the final word representation, and to find the most important word for classification through the maximum pooling layer, thereby improving the performance of text classification. The experimental results show that the algorithm proposed in this paper has better classification accuracy than other classification algorithms when the number of iterations is large.

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

Citation Download Citation

Jie Zhang, You Li, Huanhuan Li, Pan Gao, Dajian Deng, and Jianwei Hao "Text classification method based on improved long short term memory network", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310528 (27 March 2024); https://doi.org/10.1117/12.3026902

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