Hardness-guided domain adaptation to recognize biomedical named entities under low-resource scenarios
dc.contributor.author | Buntine, Wray | |
dc.contributor.author | Chen, Changyou | |
dc.contributor.author | Beare, Richard | |
dc.contributor.author | nguyen, Ngoc Dang | |
dc.contributor.author | Du, Lan | |
dc.date.accessioned | 2024-10-24T07:38:13Z | |
dc.date.available | 2024-10-24T07:38:13Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://vinspace.edu.vn/handle/VIN/290 | |
dc.description.abstract | Domain 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.iso | en_US | en_US |
dc.title | Hardness-guided domain adaptation to recognize biomedical named entities under low-resource scenarios | en_US |
dc.type | Article | en_US |
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Wray Buntine, PhD. [10]
College of Engineering and Computer Science Director, Computer Science program