@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://aclanthology.org/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.",
}
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%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
Markdown (Informal)
[NeuroNER: an easy-to-use program for named-entity recognition based on neural networks](https://aclanthology.org/D17-2017) (Dernoncourt et al., EMNLP 2017)
ACL