Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

The discovery and development of new medicines is expensive, time-consuming, and often
inefficient, with many failures along the way. Powered by artificial intelligence (AI), language …

Motivation Biomedical text mining is becoming increasingly important as the number of
biomedical documents rapidly grows. With the progress in natural language processing …

Despite recent advances in natural language processing, many statistical models for
processing text perform extremely poorly under domain shift. Processing biomedical and …

Adaptive gradient methods that rely on scaling gradients down by the square root of
exponential moving averages of past squared gradients, such RMSProp, Adam, Adadelta …

Objective Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-
the-art results on clinical natural language processing (NLP) tasks. One of the core …

The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …

Biomedical information extraction (BioIE) is an important task. The aim is to analyze
biomedical texts and extract structured information such as named entities and semantic …

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

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant
biomedical knowledge in scientific literature to understand the disease mechanism and …