Abstract Biomedical Entity Linking (BEL) is the task of mapping of spans of text within
biomedical documents to normalized, unique identifiers within an ontology. This is an …

Despite the widespread success of self-supervised learning via masked language models
(MLM), accurately capturing fine-grained semantic relationships in the biomedical domain …

Motivation Biomedical entity linking (BEL) is the task of grounding entity mentions to a
knowledge base (KB). It plays a vital role in information extraction pipelines for the life …

This survey presents a comprehensive description of recent neural entity linking (EL)
systems developed since 2015 as a result of the “deep learning revolution” in natural …

Large Language Models (LLMs) have substantially driven scientific progress in various
domains, and many papers have demonstrated their ability to tackle complex problems with …

Injecting external domain-specific knowledge (eg, UMLS) into pretrained language models
(LMs) advances their capability to handle specialised in-domain tasks such as biomedical …

Objective Research on pharmacovigilance from social media data has focused on mining
adverse drug events (ADEs) using annotated datasets, with publications generally focusing …

The existence of unlinkable (NIL) entities is a major hurdle affecting the performance of
Named Entity Linking approaches, and, consequently, the performance of downstream …

Abstract Background and Objective Automatic clinical coding is a crucial task in the process
of extracting relevant information from unstructured medical documents contained in …

Motivation Clinical trials are the essential stage of every drug development program for the
treatment to become available to patients. Despite the importance of well-structured clinical …