Natural Language Understanding
provides an NLU model builder
and an NLU inference service that you can use to enable the system to learn and
respond to human-expressed intent. By entering natural language examples into the
system, you help it to understand word meanings and contexts so it can infer user or
In NLU parlance, these terms identify the key language components the system uses to classify,
parse, and otherwise process natural language content.
- A natural language example of a user intent. For example, a text string in an incident's
short description, a chat entry, or an email subject line.
- Something a user wants to do or what you want your application to handle, such as granting
- The object of, or context for, an action. For example: a laptop, a user role, a priority
level, or an instance.
- Common Entity
- A context commonly used and extracted via a pre-defined entity model, such as date, time,
currency, location, organization, people, or quantity.
- NLU Model
- A collection of utterance examples and their associated intents and entities that the
system uses as a reference to infer intents and entities in a new utterance. You can create
default models tailored to business unit consumers, such as an ITSM Model, a CSM Model, a
Federal Model, or a Boeing Model.
This image illustrates how Natural Language Understanding processes and
renders utterance examples into intents and entities in the system.
NLU model builder
Use the NLU model builder to create
morphological representations of human language. These models enable you to create intents and
entities expressed in natural language utterances. Any ServiceNow application can invoke an NLU
model to get an inference of intents and entities in a given utterance.
Using the Admin role or the Delegated Developer role (with permission of All File Types), you
build your models in the ServiceNow
Studio, where you create, train, test,
validate, and publish them iteratively. For more information on how to use Studio, see: ServiceNow Studio.
For information on how to build and use an NLU model, see: Create an NLU model.
NLU inference service
Natural Language Understanding provides an
NLU inference service that helps the system to understand natural language and drive intelligent
actions. This service trains and predicts intents and entities for a given user utterance in
your model so that its text translates into machine-understandable formats, such as APIs and
Here, the system uses an inference API to train its NLU algorithms using sample record data so
that it can identify intents and entities that are strong candidates for prediction.
NLU model consumption
Other ServiceNow® applications
consume NLU model output, such as Virtual Agent.
For example, Virtual Agent
administrators can configure a Virtual Agent Designer conversation flow to
consume NLU models so that agent chatbots can better understand user statements in the
conversation. For more information on how Virtual Agent consumes NLU models, see: Natural Language Understanding in Virtual Agent.