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arXiv · 2106.07583

Biomedical Entity Linking with Contrastive Context Matching

Shogo Ujiie·Hayate Iso·Eiji Aramaki
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Abstract

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.

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Shogo Ujiie, Hayate Iso, Eiji Aramaki. 2021-06-14. Biomedical Entity Linking with Contrastive Context Matching. https://arxiv.org/abs/2106.07583

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