A multi-feature Fusion Model Based on Long and Short Term Memory Network and Improved Artificial Bee Colony Algorithm for English Text Classification

Tianying Wen¹

Department of Education, Liaoning National Normal College
No. 45, Chongdong Road, Huanggu District, Shenyang, 110032, China
sarkozyteague@foxmail.com

Abstract

The traditional methods of English text classification have two disadvantages. One is that they cannot fully represent the semantic information of the text. The other is that they cannot fully extract and integrate the global and local information of the text. Therefore, we propose a multi-feature fusion model based on long and short term memory network and improved artificial bee colony algorithm for English text classification. In this method, the character-level vector and word-level vector representations of English text are calculated using a pre-training model to obtain a more comprehensive text feature vector representation. Then the multi-head attention mechanism is used to capture the dependencies in the text sequence to improve the semantic understanding of the text. Through feature fusion, the channel features are optimized and the spatial features and time series features are combined to improve the classification performance of the hybrid model. In the stage of network training, the weighted linear combination of maximum Shannon entropy and minimum cross entropy is used as the return degree evaluation function of the bee colony algorithm, and the scale factor is introduced to adjust the solution search strategy of leading bees and following bees, and the improved artificial bee colony algorithm is combined with the classification network to realize the automatic optimization and adjustment of network parameters. Experiments are carried out on public data set. Compared with traditional convolutional neural networks, the classification accuracy of the new model increases by 2% on average, and the accuracy of data set increases by 2.4% at the highest.

Key words

English text classification, multi-feature fusion, artificial bee algorithm, long and short term memory network, multi-head attention mechanism

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS240314050W

Publication information

Volume 21, Issue 4 (September 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wen, T.: A multi-feature Fusion Model Based on Long and Short Term Memory Network and Improved Artificial Bee Colony Algorithm for English Text Classification. Computer Science and Information Systems, Vol. 21, No. 4, 1607–1627. (2024), https://doi.org/10.2298/CSIS240314050W