Biomedical Entity Linking with Contrastive Context Matching [article]

Shogo Ujiie, Hayate Iso, Eiji Aramaki
2021 arXiv   pre-print
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
more » ... ly outperforms state-of-the-art models, especially in low-resource settings, by effectively using the context of the entities.
arXiv:2106.07583v2 fatcat:4c62l7ekwjc3fasolnhwo75iee