Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Zhenxi Lin Ziheng Zhang Xian Wu Yefeng Zheng
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.