Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
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 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.
BibTeX
@inproceedings{nguyen-etal-2022-hardness,
title = "Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios",
author = "Nguyen, Ngoc Dang and
Du, Lan and
Buntine, Wray and
Chen, Changyou and
Beare, Richard",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.271/",
doi = "10.18653/v1/2022.emnlp-main.271",
pages = "4063--4071"
}