Amazon Comprehend: Custom Entity Recognition

To use Amazon Comprehend’s custom entity recognition:

provide a data set for model training purposes, with either a set of annotated documents, or a list of entities and their type label (such as PRODUCT_CODES) and a set of documents containing those entities.
You can train a model on up to 12 custom entities at once.
Once your model is trained, you can search for up to 12 of those custom entities you trained for, in each entities detection job.

analyze your documents to find entities specific to your needs, rather than limiting you to the preset entity types already available.
You can identify almost any kind of entity, simply by providing a sufficient number of details to train your model effectively.

Annotations
- uses an annotation list that provides the location of your entities in a large number of documents so Amazon Comprehend can train on both the entity and its context.
- increase the accuracy – more accurate context to the custom entity you’re seeking
- When the meaning of the entities could be ambiguous and context-dependent.
  - Amazon could either refer to the river in Brazil, or the online retailer Amazon.com
Entity Lists
- This provides only a limited context, and uses only a list of the specific entities list so Amazon Comprehend can train to identify the custom entity.
- comma-separated value (CSV) file
  - Text – The text of an entry example exactly as seen in the accompanying document corpus
    - např. Brno
  - Type – The customer-defined entity type.
    - uppercase, underscore separated string such as MANAGER or SENIOR_MANAGER
    - Up to 12 entity types can be trained per model.
    - A minimum of 200 entity matches are needed per entity
    - např. CITY

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