Named Entity Recognition (NER) is a crucial task in natural language processing that identifies and classifies named entities in text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

Key Features

  • Automatic Entity Detection: Automatically detect entities within text.
  • Customizable Categories: Define and train your own entity recognition models.
  • Integration with Other Tools: Seamless integration with other NLP tools and services.

How to Use

  1. Input Text: Provide the text you want to analyze.
  2. Select Model: Choose the NER model that best fits your needs.
  3. Analyze: Click the 'Analyze' button to see the detected entities.

Example

Here's an example of NER in action:

Input Text: "Apple Inc. is an American multinational technology company headquartered in Cupertino, California."

Detected Entities:
- Apple Inc. (ORG)
- American (GPE)
- Cupertino (GPE)
- California (GPE)

Further Reading

For more information on NLP Tools and Named Entity Recognition, check out our comprehensive guide: /nlp_tutorials/ner_guide/

NER in Action