NeuroNER
An easy-to-use, state-of-the-art named-entity recognition (NER) tool based on neural networks.
What is NeuroNER?
NeuroNER is an open-source named-entity recognition (NER) tool that uses neural networks to identify and classify entities like persons, organizations, and locations in text. It solves the problem of extracting structured information from unstructured text with state-of-the-art accuracy while being designed for ease of use.
Researchers, data scientists, and developers working on natural language processing tasks who need a reliable, high-performance NER system without extensive deep learning expertise.
Developers choose NeuroNER because it combines academic-grade performance (e.g., matching top scores on benchmarks like CoNLL-2003) with a simple installation process and flexible usage via command line or Python, all in an open-source package.
Overview
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
Use Cases
Best For
- Extracting medical entities from clinical notes for de-identification
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