Natural Language Understanding
Summarize
Summary of Natural Language Understanding
ServiceNow® Natural Language Understanding (NLU) enables your system to interpret and respond to human language by learning intents and contexts from natural language examples. It helps translate user inputs, such as chat entries or incident descriptions, into actionable intents and entities that your applications can process.
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Key Features
- NLU Workbench: A tool for building, training, testing, and publishing NLU models that represent human language through intents, entities, and utterances. It supports creating both custom and pre-built models tailored for specific business units like ITSM.
- NLU Inference Service: This service analyzes user utterances to predict intents and extract relevant entities, converting text into machine-understandable formats such as API parameters. It uses sample data to continually improve prediction accuracy.
- Terminology:
- Intent: The action or goal expressed by the user.
- Utterance: A natural language example illustrating an intent.
- Entity: Contextual objects or details related to an action (e.g., a laptop or priority level).
- Vocabulary: Definitions or synonyms to refine word meanings within models.
- Model Consumption: Other ServiceNow applications, such as Virtual Agent, leverage NLU models to better understand user input and improve conversational interactions.
Practical Use and Next Steps
ServiceNow customers can use NLU to enhance automation and user interaction by creating models that accurately interpret user intents and entities. Begin by exploring NLU concepts, then create and test your models in the NLU Workbench. Integrate these models into applications like Virtual Agent to enable smarter, context-aware conversations.
For assistance, customers can access community resources, known error articles, and ServiceNow Customer Support.
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
For an overview of Natural Language Understanding, see Exploring Natural Language Understanding.
NLU terminology
- 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.
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.
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.
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.
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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