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Natural Language Understanding

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Natural Language Understanding

ServiceNow® 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 system actions.

Shows you an image of the user input flow in the NLU model build process.

NLU terminology

In NLU parlance, these terms identify the key language components the system uses to classify, parse, and otherwise process natural language content.
Utterance
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.
Intent
Something a user wants to do or what you want your application to handle, such as granting access.
Entity
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.

Shows you an image of 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.

Shows you an image of how the NLU Authoring API helps NLU administrators build their NLU models.

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 parameters.

Shows you an image of how the system uses an NLU inference API to extract intents and entities for a given utterance.

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.

Shows you how the Virtual Agent application consumes Natural Language Understanding.

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.

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