Named Entity Recognition (NER)
Named Entity Recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories.
Key Points
- Identify and classify named entities: people, organizations, locations
- RESTful API with JSON responses for easy integration
- Process single documents or batch requests
- Suitable for NLP pipelines, search indexing, and analytics
- Returns entity type, position, and confidence score
Example
python
import requests
response = requests.post(
"https://api.entity-detector.com/v1/analyze",
headers={"Authorization": "Bearer YOUR_API_KEY"},
json={"text": "Elon Musk founded SpaceX in California."}
)
entities = response.json()["entities"]
print(entities)
# {"persons": ["Elon Musk"], "organizations": ["SpaceX"], "locations": ["California"]}Frequently Asked Questions
What is the difference between NER and entity extraction?
NER specifically refers to identifying named entities (proper nouns) while entity extraction is a broader term that may include extracting other structured information like dates, quantities, or custom entity types.
How do I integrate NER into my application?
Our NER API provides a simple REST endpoint. Send a POST request with your text and receive structured JSON with identified entities. See our documentation for code examples in multiple languages.
Try Entity Extraction Now
See entity extraction in action with our interactive demo or start building with the API.