Improving Biomedical Entity Linking with Retrieval-enhanced Learning

• URL: http://arxiv.org/abs/2312.09806v1
• Date: Fri, 15 Dec 2023 14:04:23 GMT
• Title: Improving Biomedical Entity Linking with Retrieval-enhanced Learning
• Authors: Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng
• Abstract summary: $k$NN-BioEL provides a BioEL model with the ability to reference similar instances from the entire training corpus as clues for prediction. We show that $k$NN-BioEL outperforms state-of-the-art baselines on several datasets.
• Score: 53.24726622142558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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