This tool identifies and classifies Named Entities, or objects with proper names, in text passages. For example, "Barack Obama" and "New York City" are both Named Entities; "president" and "city" are not.
It performs best on general purpose knowledge, such as articles from sources like The New York Times, The Economist, and Foreign Affairs.
Ardis assigns you batches of 10 entities to label: Label your first batch, and later tasks will come with recommended labels.
When you submit tasks, we update our label recommendations and choose your next batch of highest-value tasks.
Submit a batch of tasks, and see the effect immediately as we update our label recommendations for hundreds of the entities in the training dataset.
Add or remove labels to your label set at any time.
Customize your NER model using your own text data.
Call our API to use your custom NER model in your NLP projects.