More than 50 000 papers have been published about COVID-19 since the beginning of
2020 and several hundred new papers continue to be published every day. This incredible …

Abstract Biomedical Entity Linking (BEL) is the task of map** 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 …

Pretrained language models have served as important backbones for natural language
processing. Recently, in-domain pretraining has been shown to benefit various domain …

In biomedical natural language processing, named entity recognition (NER) and named
entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical …

Pre-trained language models (LMs) have become ubiquitous in solving various natural
language processing (NLP) tasks. There has been increasing interest in what knowledge …

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 …

Automatic ICD coding is defined as assigning disease codes to electronic medical records
(EMRs). Existing methods usually apply label attention with code representations to match …

Objective This paper aims to propose knowledge-aware embedding, a critical tool for
medical term normalization. Methods We develop CODER (Cross-lingual knowledge …

Abstract Automatic International Classification of Diseases (ICD) coding aims to assign
multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is …