@InProceedings{dernoncourt-lee-szolovits:2017:EMNLP2017Demos,
  author    = {Dernoncourt, Franck  and  Lee, Ji Young  and  Szolovits, Peter},
  title     = {NeuroNER: an easy-to-use program for named-entity recognition based on neural networks},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {97--102},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/D17-2017}
}