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.
@article{arxiv.1705.05487,
title = {NeuroNER: an easy-to-use program for named-entity recognition based on neural networks},
author = {Franck Dernoncourt and Ji Young Lee and Peter Szolovits},
journal= {arXiv preprint arXiv:1705.05487},
year = {2017}
}
Comments
The first two authors contributed equally to this work