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Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios

2022-11-11 03:11:21
Ngoc Dang Nguyen, Lan Du, Wray Buntine, Changyou Chen, Richard Beare

Abstract

Domain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic characteristics that clinical narratives possess, which leads to unsatisfactory performance. In this paper, we present a simple yet effective hardness-guided domain adaptation (HGDA) framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in low-resource scenarios. Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published state-of-the-art (SOTA) MetaNER model

Abstract (translated)

URL

https://arxiv.org/abs/2211.05980

PDF

https://arxiv.org/pdf/2211.05980.pdf


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