NeuroNER: an easy-to-use program for named-entity recognition based on neural networks

Franck Dernoncourt, Ji Young Lee, Peter Szolovits


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.
Anthology ID:
D17-2017
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–102
Language:
URL:
https://www.aclweb.org/anthology/D17-2017
DOI:
10.18653/v1/D17-2017
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-2017.pdf