Natural Language Understanding

  • Release version: Yokohama
  • Updated January 30, 2025
  • 4 minutes to read
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    Summary of Natural Language Understanding

    ServiceNow® Natural Language Understanding (NLU) enables your system to interpret and respond to human-expressed intents through natural language input. By providing example utterances, the system learns word meanings and context to infer user or system actions. The NLU Workbench allows you to create, train, test, and publish models that classify intents and extract entities from natural language inputs. These models can be invoked by any ServiceNow application to improve interaction accuracy and automation.

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    Key Features

    • NLU Terminology: Key concepts include Intents (user goals), Utterances (example phrases), and Entities (contextual objects such as dates, roles, or priorities). System entities are predefined, while user-defined entities can be created for custom needs.
    • NLU Workbench: A centralized tool for building and managing NLU models with iterative development phases: creation, training, testing, and publishing. It supports morphological representations of language for accurate intent and entity recognition.
    • NLU Inference Service: Translates user utterances into machine-understandable formats by predicting intents and entities, enabling automated actions and integration with APIs.
    • Model Consumption: Other ServiceNow applications, such as Virtual Agent, consume NLU model outputs to enhance chatbot understanding and conversational flows.
    • Multilingual Support: Manage NLU models in multiple languages with consistent structure across languages, ensuring a unified user experience.
    • Integration with Virtual Agent: Administrators can update and manage NLU models directly within the Virtual Agent Designer interface for streamlined chatbot configuration.

    Practical Benefits for ServiceNow Customers

    • Enable intelligent automation by accurately interpreting user intents and contextual information through natural language input.
    • Improve virtual agent and chatbot performance by leveraging NLU models for precise understanding of user statements.
    • Streamline model lifecycle management with the NLU Workbench, facilitating continuous improvement and adaptation to evolving language use.
    • Support global user bases by implementing multilingual NLU models that maintain consistent intent and entity recognition across languages.
    • Integrate NLU capabilities into diverse ServiceNow applications to enhance user experience and operational efficiency.

    Getting Started and Support

    • Explore NLU concepts and features to understand how to create and apply models effectively.
    • Use the NLU Workbench to build, test, and publish models tailored to your organizational needs.
    • Leverage Virtual Agent integration to update NLU models within conversational design workflows.
    • Access community resources, known error articles, and ServiceNow Customer Service and Support for troubleshooting assistance.

    ServiceNow® Natural Language Understanding (NLU) provides an NLU Workbench 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 understand word meanings and contexts so it can infer user or system actions.

    Overview of Natural Language Understanding

    Figure 1. User input flow in the NLU model build process
    This image shows you the user input flow in the NLU model build process.
    This image shows you 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.
    Intent
    Something a user wants to do or what you want your application to handle, such as granting access.
    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. Utterances are used to build and train intents and should therefore not include several or ambiguous meanings or intents.
    Entity
    The object of, or context for, an action. For example: a laptop, a user role, or a priority level.
    System entity
    These are predefined in an instance and have highly reusable meanings, such as date, time, and location.
    User defined entity
    These are created in the system by users and can be built from words in the utterances they create.
    Common Entity
    A context commonly used and extracted via a pre-defined entity model, such as currency, organization, people, or quantity.
    Vocabulary
    Vocabulary is used to define or overwrite word meanings. For example, you can assign the synonym “Microsoft” to the acronym “MS”.
    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. The NLU Workbench comes with pre-built NLU models for specific business units, such as an ITSM model. You can also create custom models.

    This image illustrates how Natural Language Understanding processes and renders utterance examples into intents and entities in the system.

    Figure 2. NLU processes and renders utterance examples into intents and entities
    This image shows you how Natural Language Understanding processes and renders utterance examples into intents and entities in the system.

    NLU Workbench

    Use the NLU Workbench 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 nlu_admin role, you build your models in the NLU Workbench, where you create, train, test, and publish them iteratively.

    Figure 3. Overview of NLU Authoring API helping administrators build their models
    This image shows you 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.

    Figure 4. Overview of how the system uses an NLU inference API to extract intents and entities
    This image shows you how the system uses an NLU inference API to extract intents and entities for a given utterance. This image shows you 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 NLU algorithms by using sample record data to identify intents and entities that are strong candidates for accurate prediction.

    NLU model consumption

    Other ServiceNow® applications consume NLU model output, such as Virtual Agent.

    Figure 5. Overview of Virtual Agent application consuming NLU
    This image 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 (NLU) topic discovery in Virtual Agent.

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