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

## TL;DR

NeuroNER is a user-friendly tool that simplifies training neural network-based named-entity recognition models through an intuitive web interface, making advanced NER accessible to non-experts.

## Contribution

The paper introduces NeuroNER, a GUI-based system that streamlines the process of annotating, training, and predicting NER using neural networks for users without deep technical expertise.

## Key findings

- NeuroNER outperforms traditional NER systems in accuracy.
- The tool enables rapid annotation and training cycles.
- Accessible to users without programming experience.

## 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.