A ZyLAB ONE entity is an abstraction defined on top of word(s), phrase(s), sentence(s), paragraph(s) or whole document text(s).
An example of an entity is 'John Doe'. The entity 'John Doe' belongs to the entity type 'person'.
Another example of an entity is 'New York'. The entity 'New York' belongs to the entity type 'location'.
Entities are identified with extraction rules.
An extraction rule defines all required information for the entity extraction proces: Entity Name, Extraction Type, Extraction Context.
The method of extraction is based on one of the following extraction types:
Entities are extracted with a full text query.
Regex (regular expression)
Entities are extracted with a regular expression.
For example, IBAN:
Entities are extracted with a query of up to 10,000 characters.
Entities are extracted with an exact match for a list of values.
Named Entity Recognition
Named Entities refer to terms that represent real-world objects like people, places, organizations, and so on. NER is used to identify and segment the named entities and classify or categorize them under various predefined classes.
PTM (ZyLAB Professional Text Mining)
Entities are extracted with PTM.
An entity type is a collection of entities that share a common definition.
For example, the entity type 'Person' can contain multiple entities, where each entity defines a specific person.
Other examples of entity types are Location, Country, Event, Holidays, Language and much more.