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Pseudo-labelling Enhanced Media Bias Detection

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arxiv 2107.07705 v1 pith:L6T5VEE2 submitted 2021-07-16 cs.CL cs.IRcs.LG

Pseudo-labelling Enhanced Media Bias Detection

classification cs.CL cs.IRcs.LG
keywords datadetectiondistanteffectivemethodmodelspseudo-labellingsupervision
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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