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://aclanthology.org/D17-2017
DOI:
10.18653/v1/D17-2017
Bibkey:
Cite (ACL):
Franck Dernoncourt, Ji Young Lee, and Peter Szolovits. 2017. NeuroNER: an easy-to-use program for named-entity recognition based on neural networks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 97–102, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks (Dernoncourt et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-2017.pdf
Data
CoNLL-2003