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        <title>Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios</title>
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    <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 unsatsifactory performance. In this paper, we present a simple yet effective hardness-guided domain adaptation 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-the-art (SOTA) MetaNER model.</abstract>
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%0 Conference Proceedings
%T Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
%A Nguyen, Ngoc Dang
%A Du, Lan
%A Buntine, Wray
%A Chen, Changyou
%A Beare, Richard
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F nguyen-etal-2022-hardness
%X 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 hardness-guided domain adaptation 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-the-art (SOTA) MetaNER model.
%R 10.18653/v1/2022.emnlp-main.271
%U https://aclanthology.org/2022.emnlp-main.271
%U https://doi.org/10.18653/v1/2022.emnlp-main.271
%P 4063-4071

Markdown (Informal)

[Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios](https://aclanthology.org/2022.emnlp-main.271) (Nguyen et al., EMNLP 2022)

• Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios (Nguyen et al., EMNLP 2022)

ACL

• Ngoc Dang Nguyen, Lan Du, Wray Buntine, Changyou Chen, and Richard Beare. 2022. Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4063–4071, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.