Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
Ngoc Dang Nguyen, Lan Du, Wray Buntine, Changyou Chen, Richard J Beare · 2022
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 unsatsifactory performance.In this paper, we present a simple yet effective hardnessguided domain adaptation (HGDA) framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in the low-resource scenarios.Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published state-of-theart (SOTA) MetaNER model.