Improving Biomedical Entity Linking with Retrieval-Enhanced Learning
Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng
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 kNN-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 kNN-BioEL outperforms state-of-the-art baselines on several datasets.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
BibTeX
@inproceedings{icassp2024_improvingbiomedi,
title = {Improving Biomedical Entity Linking with Retrieval-Enhanced Learning},
author = {Zhenxi Lin and Ziheng Zhang and Xian Wu and Yefeng Zheng},
booktitle = {ICASSP 2024},
year = {2024}
}