Research on the classification of online question text based on deep learning

Shan Gui

doi:10.1117/12.3011851

7 December 2023 Research on the classification of online question text based on deep learning

Shan Gui

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294129 (2023) https://doi.org/10.1117/12.3011851
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In view of the current problems of untimely information updates, incomplete interactive responses and impractical classification systems often arising from the question-and-answer sections of government websites, this paper provides a research direction for the organic integration of online question-and-answer and text classification by taking natural language processing technology as the research perspective and combining the construction and application of deep learning algorithm models. This paper improves the algorithms in text representation and deep learning models from the perspective of text representation and feature extraction. In response to the shortcomings of Bert model which weakens the text location information, LSTM model can effectively learn the dependencies on the observation sequence and extract the global features of the preceding and following texts. Therefore, this paper combines the Bert and LSTM models for feature extraction and introduces the Attention mechanism to filter and fuse the extracted features in depth to build the Bert-LSTM model. The classification results of this model and the Bert model on the same dataset are compared, and it is proved that the new hybrid depth model can improve the classification accuracy of the questioned text.

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

Citation Download Citation

Shan Gui "Research on the classification of online question text based on deep learning", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294129 (7 December 2023); https://doi.org/10.1117/12.3011851

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