Named Entity Recognition: What’s in a Name?

When we read text, we can easily identify names of people, places, organizations, or dates. But for a computer, all text looks like a sequence of words. Named Entity Recognition (NER) is the technique that helps machines automatically find and classify these special words.

What is Named Entity Recognition (NER)?

NER is a task in Natural Language Processing (NLP) that identifies and categorizes key information (entities) in text into predefined categories such as:

Person names → “Albert Einstein”

Organizations → “Google”, “United Nations”

Locations → “Hyderabad”, “Amazon River”

Dates & Times → “August 26, 2025”

Monetary values → “$1,000”, “₹50 crore”

Example of NER

Text: “Elon Musk founded SpaceX in California in 2002.”

NER Output:

Elon Musk → Person

SpaceX → Organization

California → Location

2002 → Date

Why is NER Important?

NER helps transform unstructured text into structured information. This makes it easier to search, analyze, and use in real-world applications.

Real-World Applications of NER

Search Engines → Highlight people, places, and events in search results.

Chatbots & Virtual Assistants → Understand when users mention names or locations.

Healthcare → Identify diseases, drugs, and patient information in medical records.

Finance → Detect company names, stock tickers, and monetary values in reports.

News Analysis → Extract key entities from articles to track events and trends.

✅ In short:

Named Entity Recognition is about teaching machines to recognize “who,” “where,” and “when” in text. It’s the bridge between raw words and meaningful information.

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