What NER does

Named Entity Recognition identifies and labels entities in text:

• PERSON (“Elon Musk”)
• ORG (“OpenAI”)
• GPE/LOC (“India”)
• DATE (“Jan 31”)

  flowchart LR
  T[Text] --> N[NER model] --> E[Entities + labels]

Why NER is useful

NER helps turn unstructured text into structured data for:

• search
• analytics dashboards
• knowledge graphs
• automation workflows

Approaches

• rule-based patterns (fast but brittle)
• classical ML sequence labeling
• deep learning (BiLSTM/CRF, Transformers)

Practical note

In Python, popular tools include spaCy and Hugging Face transformers.

You can learn the concept without installing heavy dependencies.

Mini-checkpoint

What’s the difference between NER and sentiment analysis?

• NER extracts entities; sentiment predicts an overall label.