A text classification model for dynamic fusion of global and local features
Existing text classification models insufficiently utilize global and local information in texts, leading to subpar classification performance. In response to this issue, a text classification model called global and local features dynamic fusion (GLFDF) is proposed. The GLFDF model was initially de...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of XPU
2024-08-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
| Subjects: | |
| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1489 |
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| Summary: | Existing text classification models insufficiently utilize global and local information in texts, leading to subpar classification performance. In response to this issue, a text classification model called global and local features dynamic fusion (GLFDF) is proposed. The GLFDF model was initially designed with a dynamic fusion enhancement module to dynamically control the integration of global temporal features and local semantic features into specific positions of the word embedding matrix. Subsequently, the embedding matrix where global and local features fused was fed into a feature extraction module for further processing. Finally, the proposed model was tested on two public datasets, Ohsumed and THUCNews. Experimental results show that the GLFDF model achieves F1 scores of 63.24% and 92.50% on these datasets, respectively, surpassing other advanced text classification models and enhancing text classification performance. From the analysis of the ablation experiment, the dynamic fusion enhancement module fully makes the global temporal features and local semantic features of the text fused together, effectively solving the problem of insufficient use of global and local information in the text classification model. |
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| ISSN: | 1674-649X |