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NLP BasicsLesson 5
Lesson 57 min

Named Entity Recognition

Pulling structured facts (names, dates, places) out of text.

What you will learn
  • ✓What NER extracts
  • ✓Classic tools vs LLM extraction
  • ✓Where it's used

Explanation

Named Entity Recognition (NER) finds and labels key items in text — people, organisations, dates, money, locations.

Classic libraries like spaCy do this well and cheaply. Today you can also prompt an LLM to extract entities into a JSON schema, which is flexible for custom fields.

NER turns unstructured text into structured data you can store and search.

Code Example

text
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Extract entities as JSON: { people: [], orgs: [], dates: [] }
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Text: 'Mirza joined StudyAI on 3 June 2026.'
Real-world use

Resume parsers use NER to pull names, employers, and dates into a structured profile automatically.

Common mistakes
  • • Using a huge LLM for simple, high-volume NER when a fast library would do.
Practice

Write one sentence and list the entities (people, orgs, dates) an NER system should extract.

Knowledge check
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