  Clinical text is rich in information, with mentions of treatment, medication
and anatomy among many other clinical terms. Multiple terms can refer to the
same core concepts which can be referred as a clinical entity. Ontologies like
the Unified Medical Language System (UMLS) are developed and maintained to
store millions of clinical entities including the definitions, relations and
other corresponding information. These ontologies are used for standardization
of clinical text by normalizing varying surface forms of a clinical term
through Biomedical entity linking. With the introduction of transformer-based
language models, there has been significant progress in Biomedical entity
linking. In this work, we focus on learning through synonym pairs associated
with the entities. As compared to the existing approaches, our approach
significantly reduces the training data and resource consumption. Moreover, we
propose a suite of context-based and context-less reranking techniques for
performing the entity disambiguation. Overall, we achieve similar performance
to the state-of-the-art zero-shot and distant supervised entity linking
techniques on the Medmentions dataset, the largest annotated dataset on UMLS,
without any domain-based training. Finally, we show that retrieval performance
alone might not be sufficient as an evaluation metric and introduce an article
level quantitative and qualitative analysis to reveal further insights on the
performance of entity linking methods.
