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Named entity recognition

**Named entity recognition (NER)** finds and labels entities in text—people, organizations, places, dates, products, and domain types you define—so systems can structure messy language. SLP3 places sequence labeling for parts of speech a...

What it is

Named entity recognition (NER) finds and labels entities in text—people, organizations, places, dates, products, and domain types you define—so systems can structure messy language. SLP3 places sequence labeling for parts of speech and named entities in its linguistic-structure volume; Yadav & Bethard (COLING 2018) survey how deep models changed NER after decades of feature-engineered systems.

<!-- IMAGE: sentence with highlighted PERSON / ORG / LOC spans -->

HIGH PRIORITYANNOTATED SCREENSHOT-STYLE
◷ IN PRODUCTION

Visual Spec & Architecture Diagram

Annotated sentence with colored entity spans: 'Alice Chen [PERSON] joined Acme Corp [ORG] in Paris [LOC] on March 3, 2024 [DATE]'. BIO/IOB tag row underneath: B-PER I-PER O B-ORG I-ORG ... Legend for PERSON/ORG/LOC/DATE. Fake names only.

Educational Focus: NER is span labeling—must see tags on text, not just definitions.

Why it matters

Search, compliance, CRM enrichment, and analytics often need entities more than chat paragraphs. Span mistakes break pipelines even when a chatbot sounds fluent.

How it works (plain)

  1. Split text into tokens (or characters/subwords).
  2. Label each token with a scheme such as BIO (Begin/Inside/Outside).
  3. Decode spans from tags.
  4. Evaluate with span-level precision/recall/F1—not only token accuracy.
  5. Adapt to your domain (legal, medical, tickets) with labeled examples or careful LLM extraction + review.

Neural NER (CNNs/RNNs/Transformers + CRF layers historically) largely replaced heavy hand-crafted features—but lessons from feature-based systems still help (gazetteers, cascading rules, schema design).

Everyday example

Pulling company names out of news to build a watchlist. “Jordan” might be a person, a country, or a brand—context decides.

Try it

Highlight entities in one email you wrote. Mark ambiguous cases. Write the label scheme you’d need (types + nesting rules) before picking a model.

Myths

⚠️ Myth: LLMs make NER evaluation unnecessary.
✓ Reality: Span mistakes still break pipelines—measure them.
⚠️ Myth: CoNLL-style person/org/loc covers every business.
✓ Reality: Custom types and nested entities are common.
⚠️ Myth: Token accuracy equals product quality.
✓ Reality: One bad boundary can destroy a whole span.

Sources