NLU entities
Entities provide your model with additional context when receiving user input. Add entities to your utterances and intents to improve the predictions of your Natural Language Understanding (NLU) model.
Think of entities as the object of the action that the user wants to perform. The model interprets the utterance by matching it with an intent, but also uses entities to gather more details on the request.
- HARDWARE (entity) - laptop (value)
- urgency (entity) - High (value)
NLU entities fall into two categories: system and user-defined. System entities such as DATE, TIME, and LOCATION are available by default in your instance. You can create your own user-defined entities to provide context relevant to your business.
When you create entities, annotate them on utterances to provide examples to help your model learn. By annotating entities, you provide your model with linguistic associations and meaningful context for the system vocabulary. Annotations strengthen the relevance of entities and help your model perform the correct action in response to your users' inputs.
You add entities to your utterances when you are creating the intents. The entities then become associated to that intent, giving you the Associated Intents number.
Regular expressions
Regular expressions (regex) help your model establish patterns that improve that model's ability to locate, match, and manage text. Use regular expressions with pattern entities to help your model understand formats such as email addresses, phone numbers, and incident numbers.
To learn more, see Using regular expressions in entities.
Model availability
When you create an entity, you can choose to make the entity available for reuse by other intents in the model. If you didn't select the Model availability box when creating the entity, you can edit the entity afterwards.