Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models. However, existing BioEL methods usually struggle to handle rare and difficult entities due to long-tailed distribution. To address this limitation, we introduce a new scheme $k$NN-BioEL, which provides a BioEL model with the ability to reference similar instances from the entire training corpus as clues for prediction, thus improving the generalization capabilities. Moreover, we design a contrastive learning objective with dynamic hard negative sampling (DHNS) that improves the quality of the retrieved neighbors during inference. Extensive experimental results show that $k$NN-BioEL outperforms state-of-the-art baselines on several datasets.

翻译：生物医学实体链接（BioEL）借助预训练语言模型取得了显著进展。然而，现有BioEL方法通常因长尾分布而难以处理稀有和困难实体。为解决这一局限，我们提出了一种新方案kNN-BioEL，该方案为BioEL模型提供了一种能力，使其能够从整个训练语料库中引用相似实例作为预测线索，从而提升泛化能力。此外，我们设计了一种具有动态困难负采样（DHNS）的对比学习目标，该目标在推理过程中提升了检索邻居的质量。大量实验结果表明，kNN-BioEL在多个数据集上优于现有最先进基线方法。