%0 Conference Proceedings %T NeuroNER: an easy-to-use program for named-entity recognition based on neural networks %A Dernoncourt, Franck %A Lee, Ji Young %A Szolovits, Peter %Y Specia, Lucia %Y Post, Matt %Y Paul, Michael %S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations %D 2017 %8 September %I Association for Computational Linguistics %C Copenhagen, Denmark %F dernoncourt-etal-2017-neuroner %X 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. %R 10.18653/v1/D17-2017 %U https://aclanthology.org/D17-2017 %U https://doi.org/10.18653/v1/D17-2017 %P 97-102