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    <abstract>This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (MayoSRS).</abstract>
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%0 Conference Proceedings
%T BioLORD: Learning Ontological Representations from Definitions for Biomedical Concepts and their Textual Descriptions
%A Remy, François
%A Demuynck, Kris
%A Demeester, Thomas
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F remy-etal-2022-biolord
%X This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (MayoSRS).
%R 10.18653/v1/2022.findings-emnlp.104
%U https://aclanthology.org/2022.findings-emnlp.104
%U https://doi.org/10.18653/v1/2022.findings-emnlp.104
%P 1454-1465

Markdown (Informal)

[BioLORD: Learning Ontological Representations from Definitions for Biomedical Concepts and their Textual Descriptions](https://aclanthology.org/2022.findings-emnlp.104) (Remy et al., Findings 2022)

• BioLORD: Learning Ontological Representations from Definitions for Biomedical Concepts and their Textual Descriptions (Remy et al., Findings 2022)

ACL

• François Remy, Kris Demuynck, and Thomas Demeester. 2022. BioLORD: Learning Ontological Representations from Definitions for Biomedical Concepts and their Textual Descriptions. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1454–1465, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.