  In biomedical natural language processing, named entity recognition (NER) and
named entity normalization (NEN) are key tasks that enable the automatic
extraction of biomedical entities (e.g. diseases and drugs) from the
ever-growing biomedical literature. In this article, we present BERN2 (Advanced
Biomedical Entity Recognition and Normalization), a tool that improves the
previous neural network-based NER tool by employing a multi-task NER model and
neural network-based NEN models to achieve much faster and more accurate
inference. We hope that our tool can help annotate large-scale biomedical texts
for various tasks such as biomedical knowledge graph construction.
