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NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Name
1705.05487.pdf
Description
Submitted version
Size
533.04 KB
Format
Adobe PDF
Checksum (MD5)
1cd12ba4ddd8678627264b30759b53a1
Author(s)
Dernoncourt, Franck
Lee, Ji Young
Szolovits, Peter
Date Issued
May 2017
Publisher
Association for Computational Linguistics
Citation
F Dernoncourt, et al. "NeuroNER: an easy-to-use program for named-entity recognition based on neural networks." arXiv preprint arXiv:1705.05487, 2017
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/110.18653/v1/d17-2017
