论文标题
硬度引导的领域适应以识别低资源场景下的生物医学实体
Hardness-guided domain adaptation to recognise biomedical named entities under low-resource scenarios
论文作者
论文摘要
在低资源场景中，域的适应性是解决数据稀缺的有效解决方案。但是，当应用于Bioner等令牌级任务时，域适应方法通常会遭受临床叙事所具有的具有挑战性的语言特征，从而导致表现不佳。在本文中，我们为Bioner任务提供了一个简单而有效的硬度引导的域适应（HGDA）框架，该框架可以有效利用域硬度信息来提高在低资源场景中学习模型的适应性。生物医学数据集的实验结果表明，我们的模型可以比最近发表的最先进（SOTA）Metaner模型实现显着的性能改善
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 unsatisfactory performance. In this paper, we present a simple yet effective hardness-guided domain adaptation (HGDA) framework for bioNER tasks that can effectively leverage the domain hardness information to improve the adaptability of the learnt model in 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
