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Natural Language Processing Systems • Named Entity Recognition (NER)Easy⏱️ ~2 min

What is Named Entity Recognition (NER)?

Definition
Named Entity Recognition (NER) identifies and classifies specific entities in text, like people, organizations, locations, dates, and product names, into predefined categories.
The Core Problem NER Solves
Raw text is unstructured. A search query like "flights from New York to London next Friday" contains actionable information buried in natural language. Without NER, your system sees 8 words with no semantic meaning. With NER, you extract: LOCATION(New York), LOCATION(London), DATE(next Friday). Now you can query a flight database with structured parameters instead of fuzzy text matching.
The same principle applies across domains. Customer support tickets mention product names, account numbers, and dates that need routing. Legal documents reference company names and case citations that need indexing. Medical records contain drug names and conditions that need structured storage.
Why Pattern Matching Fails
You might think regex could solve this. It cannot. Consider "Apple" - is it the company, the fruit, or Apple Records? The answer depends on context. NER models learn these contextual signals from training data, distinguishing entity types based on surrounding words and sentence structure.
💡 Key Insight: NER accuracy varies dramatically by domain. A model trained on news achieves 90%+ F1 on standard benchmarks but might drop to 60-70% on legal or medical text because entity types and language patterns differ.
Evaluation Metrics
NER evaluation distinguishes exact match (span boundaries AND entity type correct) from partial match (overlapping spans with correct type). Extracting "New York" instead of "New York Times" scores well on partial match but poorly on exact match. Both matter, but exact match is the harder, more meaningful target.

💡 Key Takeaways

✓NER transforms unstructured text into structured data by identifying entities like people, organizations, locations, and dates with their categories

✓Simple pattern matching fails because the same word (Apple, Washington) can be different entity types depending on context

✓Domain transfer is a major challenge: models trained on news may drop from 90% F1 to 60-70% on legal or medical text

✓Exact match evaluation (correct span AND type) is harder but more meaningful than partial match metrics

📌 Interview Tips

1When discussing NER, give a concrete example: 'flights from New York to London next Friday' becomes LOCATION, LOCATION, DATE - showing the transformation from text to queryable structure.

2Mention the ambiguity problem early. Ask the interviewer what domain they're working in, because entity types and accuracy expectations vary dramatically.

3Distinguish exact vs partial match metrics. A system with high partial but low exact match has boundary detection problems.

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