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Pseudo-labelling Enhanced Media Bias Detection

2021-07-16 04:47:50
Qin Ruan, Brian Mac Namee, Ruihai Dong
arXiv_CL
         
Abstract

Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models. This paper proposes a simple but effective data augmentation method, which leverages the idea of pseudo-labelling to select samples from noisy distant supervision annotation datasets. The result shows that the proposed method improves the accuracy of biased news detection models.

Abstract (translated)

URL

https://arxiv.org/abs/2107.07705

PDF

https://arxiv.org/pdf/2107.07705.pdf

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