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Plain-language AI glossary

Term 47ApplicationsMeaning / context / connections

Applications / Definition

Named Entity Recognition (NER)

An NLP technique that identifies and classifies named entities in text, such as people, organizations, locations, dates, and monetary values.

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Named Entity Recognition (NER) works.

Named entity recognition turns unstructured text into labeled entities such as people, organizations, locations, dates, money amounts, products, or domain-specific concepts. A language model can produce entities in a structured format, while a specialized classifier can be more predictable for a fixed schema and large volume.

Production uses include extracting fields from documents, improving search, linking mentions, detecting sensitive data, and building knowledge graphs. Define the entity schema precisely, test ambiguous cases, validate the returned structure, and measure both missed entities and incorrect ones before automating downstream actions.

02 / Practical uses

Where it helps.

  1. 01Invoice and receipt data extraction
  2. 02Resume parsing
  3. 03Legal document analysis
  4. 04PII detection for compliance
  5. 05Content enrichment and linking
03 / Keep exploring
Natural Language Processing (NLP)The branch of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.Structured Output / JSON ModeA feature that forces AI models to return responses in a specific format like JSON, ensuring parseable and type-safe outputs for programmatic use.Sentiment AnalysisAn NLP technique that determines the emotional tone of text, classifying it as positive, negative, neutral, or more granular emotions.Semantic SearchA search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.