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arXiv · 1705.05487

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

Franck Dernoncourt·Ji Young Lee·Peter Szolovits
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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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Franck Dernoncourt, Ji Young Lee, Peter Szolovits. 2017-05-16. NeuroNER: an easy-to-use program for named-entity recognition based on neural networks. https://arxiv.org/abs/1705.05487

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