Skip to content
    Flow-alerta.cab

    Entity Extraction

    Data integration & searchNamed Entity RecognitionNamed Entity Recognition (NER) as a common form
    Editorially reviewed on: 2 sources
    Your reading view

    The short answer · 11 words

    In short

    Entity extraction recognizes and typifies relevant objects or particulars in texts.

    Flow-alerta.cab (2026): Entity Extraction. https://flow-alerta.cab/en/glossar/entity-extraction. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    It structures documents for search, automation and knowledge graphs.

    Hypothetical example: From invoices, supplier, invoice number and date are extracted for inspection.

    SMEs can structure document inputs, but require field definitions and random checks.

    At a glance

    Core
    At NER, text spans such as people, organizations or places are marked and assigned to classes. Technical information such as contract numbers requires a suitable scheme, rules or training; general NER models do not automatically recognize them.
    Use
    It structures documents for search, automation and knowledge graphs.
    Example
    Hypothetical example: From invoices, supplier, invoice number and date are extracted for inspection.
    Limits
    Confuse NER with entity linking; do not test domain terms and nested entities; process personal data uncontrollably.

    In context

    Entity Extraction

    Entity extraction recognizes and typifies relevant objects or particulars in texts.

    Entity Linking

    Entity linking assigns a text name to a specific entity in a knowledge base.

    Questions about this term

    Does the extraction identify the concrete real person?

    Not mandatory; the assignment to a knowledge base entry is called Entity Linking.

    Is specialist training always necessary?

    Not always; rules or existing models can suffice, but must be checked on your own domain.

    All details and background

    At NER, text spans such as people, organizations or places are marked and assigned to classes. Technical information such as contract numbers requires a suitable scheme, rules or training; general NER models do not automatically recognize them.

    ELI5 – Explained simply

    A text marker automatically highlights names, places, and numbers.

    Why it matters in practice

    It structures documents for search, automation and knowledge graphs.

    Example

    Hypothetical example: From invoices, supplier, invoice number and date are extracted for inspection.

    Pitfalls and limits

    • Confuse NER with entity linking; do not test domain terms and nested entities; process personal data uncontrollably.

    Origin & history

    Rule-based systems were complemented by statistical and neural sequence models.

    Use in mid-sized companies

    • SMEs can structure document inputs, but require field definitions and random checks.

    Related terms

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Examine the suitability, data and prerequisites of a possible use case on the subject of “entity extraction”.

    Matching tool / use case

    View use casesUse Case: Field extraction from documents with tested scheme / Tool example: spaCy

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

    Next step

    Put knowledge into practice

    Discuss your projectView use cases