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BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights

A study on using large language models to complement biomedical knowledge graphs for training semantic models.

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BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights

By François Remy, Kris Demuynck, Thomas DemeesterJournal of the American Medical Informatics Association
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The authors propose a new approach for obtaining high-fidelity representations of biomedical concepts and sentences. They introduce BioLORD-2023, a state-of-the-art model for semantic textual similarity and biomedical concept representation designed for the clinical domain.

The study demonstrates consistent improvements over previous state-of-the-art models across various datasets.

Abstract

The authors propose a new approach for obtaining high-fidelity representations of biomedical concepts and sentences. They introduce BioLORD-2023, a state-of-the-art model for semantic textual similarity and biomedical concept representation designed for the clinical domain. The study demonstrates consistent improvements over previous state-of-the-art models across various datasets.

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biomedical knowledge graphslarge language modelssemantic textual similaritybiomedical concept representationclinical domainKnowledge GraphsLarge Language ModelsSemantic InteroperabilityOntology & Taxonomy
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