@inproceedings{dernoncourt-etal-2017-neuroner,
    title = "{N}euro{NER}: an easy-to-use program for named-entity recognition based on neural networks",
    author = "Dernoncourt, Franck  and
      Lee, Ji Young  and
      Szolovits, Peter",
    editor = "Specia, Lucia  and
      Post, Matt  and
      Paul, Michael",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/D17-2017/",
    doi = "10.18653/v1/D17-2017",
    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."
}