Biomedical Entity Linking with Contrastive Context Matching
This addresses entity linking in biomedical texts, which is crucial for researchers and practitioners in bioinformatics and healthcare, though it appears incremental as it builds on existing contrastive learning and dictionary-based methods.
The authors tackled biomedical entity linking by introducing BioCoM, a contrastive learning framework that uses a small dictionary and raw PubMed articles, achieving substantial performance improvements over state-of-the-art models, particularly in low-resource settings.
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.