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
- Editors:
- Lucia Specia, Matt Post, Michael Paul
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D17-2017.pdf
- Data
- CoNLL 2003