Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Automated relation extraction (RE) from biomedical literature is critical for many downstream
text mining applications in both research and real-world settings. However, most existing …

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

Objective The study sought to develop and evaluate neural natural language processing
(NLP) packages for the syntactic analysis and named entity recognition of biomedical and …

Cancer still represents one of the leading causes of death worldwide, resulting in a
considerable healthcare impact. Recent research efforts from the clinical and molecular …

This paper presents the formal release of MedMentions, a new manually annotated resource
for the recognition of biomedical concepts. What distinguishes MedMentions from other …

One effective way to improve the state of the art is through competitions. Following the
success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics …

Abstract Background Named Entity Recognition (NER) is a key task in biomedical text
mining. Accurate NER systems require task-specific, manually-annotated datasets, which …

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier
to the implementation of precision medicine that clinicians and translational scientists face is …

Named entity recognition (NER) is a widely applicable natural language processing task
and building block of question answering, topic modeling, information retrieval, etc. In the …