Before revision:  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 .
After revision:  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. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of 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 .