  We propose Medical Entity Definition-based Sentence Embedding (MED-SE), a
novel unsupervised contrastive learning framework designed for clinical texts,
which exploits the definitions of medical entities. To this end, we conduct an
extensive analysis of multiple sentence embedding techniques in clinical
semantic textual similarity (STS) settings. In the entity-centric setting that
we have designed, MED-SE achieves significantly better performance, while the
existing unsupervised methods including SimCSE show degraded performance. Our
experiments elucidate the inherent discrepancies between the general- and
clinical-domain texts, and suggest that entity-centric contrastive approaches
may help bridge this gap and lead to a better representation of clinical
sentences.
