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dc.contributor.authorBuntine, Wray
dc.contributor.authorChen, Changyou
dc.contributor.authorBeare, Richard
dc.contributor.authornguyen, Ngoc Dang
dc.contributor.authorDu, Lan
dc.date.accessioned2024-10-24T07:38:13Z
dc.date.available2024-10-24T07:38:13Z
dc.date.issued2022
dc.identifier.urihttps://vinspace.edu.vn/handle/VIN/290
dc.description.abstractDomain adaptation is a promising approach to address data scarcity in low-resource scenarios. However, applying it to token-level tasks like biomedical Named Entity Recognition (bioNER) poses challenges due to the unique linguistic characteristics of clinical narratives, often resulting in unsatisfactory performance. In this paper, we introduce a hardness-guided domain adaptation (HGDA) framework specifically designed for bioNER tasks. This framework effectively utilizes domain hardness information to enhance the adaptability of the learned model in low-resource situations. Experimental results on various biomedical datasets demonstrate that our model significantly outperforms the recently published state-of-the-art (SOTA) MetaNER model, highlighting the effectiveness of our approach in improving performance in challenging contexts.en_US
dc.language.isoen_USen_US
dc.titleHardness-guided domain adaptation to recognize biomedical named entities under low-resource scenariosen_US
dc.typeArticleen_US


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  • Wray Buntine, PhD. [10]
    College of Engineering and Computer Science Director, Computer Science program

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