We introduce BioCoM, a contrastive learning framework for biomedical entity linking that uses only two resources: a small-sized dictionary and a large number of raw biomedical articles. Specifically, we build the training instances from raw PubMed articles by dictionary matching and use them to train a context-aware entity linking model with contrastive learning. We predict the normalized biomedical entity at inference time through a nearest-neighbor search. Results found that BioCoM substantially outperforms state-of-the-art models, especially in low-resource settings, by effectively using the context of the entities.
Cite
@article{arxiv.2106.07583,
title = {Biomedical Entity Linking with Contrastive Context Matching},
author = {Shogo Ujiie and Hayate Iso and Eiji Aramaki},
journal= {arXiv preprint arXiv:2106.07583},
year = {2021}
}