期刊论文

Chen, ZZ

英文

APPLIED SCIENCES-BASEL

2076-3417

2024

14

10

4259-

10.3390/app14104259

Conceptualization, Y.T., Q.L. and Z.C.; methodology, Y.T. and Z.C.; validation, Y.T. and Q.L.; formal analysis, Y.T., Z.C. and H.L.; investigation, Y.T, Z.C. and H.L.; resources, Y.T. and Z.C.; data curation, Y.T., Q.L., Z.C. and H.L.; writing—original draft preparation, Y.T. and Q.L.; writing—review and editing, Y.T., Q.L., Z.C. and H.L.; supervision, Z.C., T.L. and H.L.; project administration, Y.T. and Z.C.; funding acquisition, Z.C., H.L., T.L. and S.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (grant No. 62277026), the Research Project of National Collaborative Innovation Experimental Base for Teacher Development of Central China Normal University (grant No. CCNUTEIII 2021-21), the National Natural Science Foundation of China (grant 62277041, grant 62211530433, grant 62177018), and in part by the National Natural Science Foundation of Hubei Province project (No. 2022CFB529, 2022CFB971), the Jiangxi Provincial Natural Science Foundation under Grant (No. 20232BAB212026), the University Teaching Reform Research Project of Jiangxi Province (Grant No. JXJG-23-27-6), and the Shenzhen Science and Technology Program under Grant (No. JCYJ20230807152900001).

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The pre-trained language model based on Transformers possesses exceptional general text-understanding capabilities, empowering it to adeptly manage a variety of tasks. However, the topic classification ability of the pre-trained language model will be seriously affected in the face of long colloquial texts, expressions with similar semantics but completely different expressions, and text errors caused by partial speech recognition. We propose a long-text topic classification method called RQ-OSPTrans to effectively address these challenges. To ...