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).

本文介绍了 BioLORD，一种用于产生临床句子和生物医学概念有意义表示的新预训练策略。BioLORD 使用来自多关系知识图谱的定义和简短描述对概念进行表示，从而得到更具语义的表示，并在临床句子和生物医学概念上取得了新的最先进的文本相似性表现。