Nlu Service

Activate the following plugins to activate the NLU Workbench.

Use update sets to move your Natural Language Understanding (NLU) models from one instance to another. Update sets include all records needed for your model to function on the target instance.

Assign an editor to review your Natural Language Understanding (NLU) model translations and edit model content. Delegate the maintenance, testing, and optimization of model content to an editor.

Test multiple Natural Language Understanding (NLU) models against a large set of utterances to evaluate the performance of the models. Add test sets, test multiple models, and see test results.

Duplicate an existing Natural Language Understanding (NLU) model to create a new one. Duplicating a model copies the settings and contents of the original model, including its default test set.

Compare a draft trained Natural Language Understanding (NLU) model to its most recent published version. Test and review the changes to make sure that your draft model will have increased performance.

Create an entity mapped to a vocabulary source, or to a list of values you manually create for the entity. Mapped entities can help provide multiple values the model can use as context when interpreting utterances.

Create one or more simple entities from words in your utterance examples. An entity is an object of, or context for, an action.

Create an intent for your Natural Language Understanding (NLU) model. Intents provide your model with a system action to perform when it receives user input.

Upload a CSV or XLSX (Excel Workbook) file containing utterances and their intents to create a Natural Language Understanding (NLU) model. Use this method to quickly create models from your data or other exported models.

Use the prebuilt Natural Language Understanding (NLU) models in the NLU Workbench to create a new one.

Create a Natural Language Understanding (NLU) model from scratch. Start with an empty model to have full control over the model's content.

Use an open-ended entity when you want to improve intent prediction accuracy. Open-ended entities help your model focus on the context of the utterances.

Create a pattern entity from a word or phrase with repeatable patterns, such as email addresses and phone numbers. These patterns help the system to recognize similar utterances based on the patterns.

Use regular expression (regex) encoding to establish a pattern format for vocabulary items such as email addresses, phone numbers, and record naming conventions. You can create your own patterns for the vocabulary data in your instance.

Add a word or phrase that your users might use, and match that vocabulary item to a synonym. Your model uses the synonym during intent prediction.

Create a list of words or phrases to act as a vocabulary source. The values in the list source are replaced by the synonym if they are detected in a user utterance.

Create a custom entity that's derived from a default system entity such as date, time, duration, or location.

Use the values from a ServiceNow table as a vocabulary source. Your Natural Language Understanding (NLU) models use your provided synonym to interpret utterances that contain values from the chosen source fields of the table.

Creating models is the first step to taking advantage of Natural Language Understanding (NLU) in your instances. Create models for Virtual Agent and AI Search in the NLU Workbench.

Delete a Natural Language Understanding (NLU) model permanently.

Enable and disable intents in your Natural Language Understanding (NLU) models to make them active or inactive. Disable intents while editors or admins edit, review, or update its content and translations.

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.

Export a Natural Language Understanding (NLU) model to create a CSV file of the intents and utterances. You can then use the CSV file to edit, share, and import.

To create or add to an NLU test set, you can upload a file of test utterances matched with correct intents. Use the test set to assess the performance of your model.

Reuse entities that you have created across your other Natural Language Understanding (NLU) models. Importing entities saves time and helps improve the intents in your model.

As you create intents for your Natural Language Understanding (NLU) model, you can also import and reuse intents from other models in the same application scope. Reusing intents saves time when building new models.

When content in your primary NLU model is updated, you can import the updates directly to secondary models.

You can install the Intent Discovery application (sn_nlu_discovery) if you have the admin role.If the application does NOT include demo data or it does NOT install related applications and plugins, delete or revise the following sentence:

You can install the NLU Workbench - Advanced Features application (com.snc.nlu.workbench.advanced) if you have the admin role.

Several types of components are installed with activation of the sn_nlu_discovery plugin, including tables.

Several types of components are installed with activation of the com.snc.nlu.workbench.advanced plugin, including tables.

Use the Intent Discovery application to help identify opportunities for incident deflection. For example, you can use it to identify which Virtual Agent conversations to activate next.

Intents drive your models' responses by matching a system action to user inputs. Models with good intents help Virtual Agent and Search respond to your users accurately.

Keep Virtual Agent chats focused with Irrelevance detection. Use the Irrelevance detection feature to train your NLU model to avoid making predictions for utterances that are not relevant.

Use the NLU Workbench homepage to support Issue Auto Resolution (IAR) tuning in NLU.

When you are tuning your Issue Auto Resolution model in NLU Workbench, you can adjust the output for several goals: precision, automation, or a balance of the two. Compare how your choice of tuning options affect match rate and coverage, before committing.

After creating a model, build the model's content by adding intents, entities, vocabulary, and test set utterances. Your NLU model content determines how the model responds to user inputs.

Language grouping makes it easier to manage your multilingual Natural Language Understanding (NLU) models. You can review existing language groups and designate new language groups.

Manage your NLU model's life cycle in the NLU Workbench. Model management phases guide you through the iterative process of building, testing, and publishing your model.

Use multilingual Natural Language Understanding (NLU) models for the system to understand user input in several languages. The NLU Workbench helps you manage and maintain a consistent structure for content across languages to provide a unified experience.

NLU Workbench is installed with these roles.

Provide feedback on Virtual Agent chat log utterances to help the system continuously learn and to better predict user input.

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.

Refer to these system properties for the Natural Language Understanding (NLU) application.

Identify conflicting intents within or across models so you can take corrective actions, resolve such conflicts, and improve your NLU model performance.

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.

The NLU Workbench application provides support for creating NLU models in different languages for use in other applications, such as Virtual Agent.

Use NLU Model Performance to see how well your models predicted intents in Virtual Agent (VA) based on end-user confirmation.

Change your NLU model's name, description, or confidence threshold on the Settings page of the model overview.

Use NLU models to apply ServiceNow Natural Language Understanding on your instances. Create, manage, test, and publish NLU models with the NLU Workbench.

Refer to this documentation so you are up to date with changes to the NLU Service.

Use the default test set of your NLU model to test the model's performance and accuracy. Manage your test set over time by building or updating its content in the NLU Workbench.

On your model's overview in NLU Workbench, open the Tune your model phase to review and incorporate user utterances from the Expert Feedback Loop.

NLU Workbench - Advanced Features expands the functionality of NLU Workbench to help you manage and improve your models.

Use ServiceNow pre-built vocabulary for software and hardware terms so the system recognizes their multiple variations in utterances.

Publish your Natural Language Understanding (NLU) model to activate it and make it available for use in other applications that consume NLU.

Use the issue cards to identify intents that have conflicts, need reviewing, or need more utterances. Resolving intent issues ensures the intents in your Natural Language Understanding (NLU) models work properly.

Reuse Natural Language Understanding (NLU) intents by importing them from a prebuilt NLU model to other models. Reusing intents saves time when building your models.

Test multiple Natural Language Understanding (NLU) models against a test set. Evaluate the quality of your models and refine them to improve intent prediction.

Synchronize your table vocabulary sources to obtain the latest changes to the ServiceNow source table. Synchronizing your vocabulary sources ensures your NLU models have the latest values when predicting intents.

When testing your NLU model on the Try model section of the test panel, use this feature to provide feedback on the model's intent predictions.

Train and try your model iteratively so that its intents and entities are validated, compiled, and saved to your model.

Test your Natural Language Understanding (NLU) model against its default test set. Testing helps determine how your model is performing with the current content.

Assess the performance of your NLU model to identify areas for improvement. Then publish your model to make it available to other applications such as Virtual Agent.

Add a language to an existing NLU model by translating it. Use one of several translation options to add a secondary model in a supported language.

Use NLU vocabulary items to help the system recognize the various ways your users express their requests. Use vocabulary sources to help the system recognize objects in tables or lists, such as names of conference rooms or catalog items.

Learn how to use regular expressions in your NLU entities to establish patterns that help the system locate, match, and manage text.

Virtual Agent administrators can access and update their NLU models from within the Virtual Agent Designer user interface.