Despite the widespread success of self-supervised learning via masked language models , learning representations directly from text to accurately capture complex and fine-grained semantic relationships in the biomedical domain remains as a challenge. Addressing this is of paramount importance for  tasks such as entity linking where complex relational knowledge is pivotal. We propose SapBERT, a pre-training scheme based on BERT. It self-aligns the representation space of biomedical entities with a metric learning objective function leveraging UMLS, a  collection of biomedical ontologies with > 4M concepts. Our experimental results on six medical entity linking benchmarking datasets demonstrate that SapBERT outperforms many domain-specific BERT-based variants such as BioBERT, BlueBERT and PubMedBERT, achieving the state-of-the-art (SOTA) performances .
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities . We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M + concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust .
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benchmarking datasets demonstrate that SapBERT outperforms many
(MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various
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BERT-based variants
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BlueBERT
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achieving the state-of-the-art (SOTA) performances
our pretraining scheme proves to be both effective and robust