  Named-entity recognition (NER) aims at identifying entities of interest in a
text. Artificial neural networks (ANNs) have recently been shown to outperform
existing NER systems. However, ANNs remain challenging to use for non-expert
users. In this paper, we present NeuroNER, an easy-to-use named-entity
recognition tool based on ANNs. Users can annotate entities using a graphical
web-based user interface (BRAT): the annotations are then used to train an ANN,
which in turn predict entities' locations and categories in new texts. NeuroNER
makes this annotation-training-prediction flow smooth and accessible to anyone.
