What is Entity Extraction?
Entity extraction (also called named entity recognition or NER) is the process of automatically identifying and classifying key elements in text into predefined categories such as person names, organizations, locations, dates, and more.
Key Points
- Automatically identify people, places, organizations, and other entities in unstructured text
- Convert raw text into structured, machine-readable data
- Essential for search engines, knowledge graphs, and AI applications
- Supports multiple languages and entity types
- Returns confidence scores and contextual information
Example
curl -X POST https://api.entity-detector.com/v1/analyze \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"text": "Apple CEO Tim Cook announced new products in Cupertino."}'Frequently Asked Questions
What types of entities can be extracted?
Common entity types include person names, organizations, locations, dates, monetary values, percentages, and more. The specific types depend on the model and configuration used.
How accurate is entity extraction?
Accuracy varies by language, domain, and text quality. Our API typically achieves 85-95% accuracy on well-formed English text. Results may vary for specialized domains or less common languages.
Can entity extraction work on any language?
Our API currently supports English with best results. Other languages may work with reduced accuracy. Check our documentation for the latest language support.
Try Entity Extraction Now
See entity extraction in action with our interactive demo or start building with the API.