NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
This addresses the problem of usability for non-experts in NER, though it is incremental as it applies existing neural network methods to improve accessibility.
The paper tackles the challenge of making neural network-based named-entity recognition accessible to non-expert users by introducing NeuroNER, a tool that simplifies the annotation-training-prediction workflow, resulting in an easy-to-use program with a graphical web interface.
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.