| Autor: |
Li, Shujie, Yuan, Guanghu, Yang, Min, Shen, Ying, Li, Chengming, Xu, Ruifeng, Zhao, Xiaoyan |
| Zdroj: |
ACM Transactions on Information Systems; Jul2024, Vol. 42 Issue 4, p1-28, 28p |
| Abstrakt: |
The article introduces a dual meta-learning (DML) technique to enhance semi-supervised text classification, improving both teacher and student classifiers iteratively. Topics include the challenge of noisy pseudo-labels in semi-supervised text classification, the proposed meta-learning methods for noise correction and pseudo supervision, and the experimental validation demonstrating the effectiveness of the DML framework. |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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