Create a regular vocabulary item

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.

시작하기 전에

• Make sure that the NLU Workbench plugin, NLU Workbench - Core plugin, NLU Common Model plugin, and Predictive Intelligence plugin are all installed and activated on your instance.
• Create or use an existing NLU model for Virtual Agent or AI Search.
• Role required: nlu_editor, nlu_admin, or admin. The editor must be assigned to the model.

이 태스크 정보

Regular vocabulary items provide the model with a synonym for words or phrases your users might use in an utterance. The model uses the synonym to replace the vocabulary when predicting the intent. Use a single word for the synonym when possible.

Regular vocabulary items are case-insensitive by default. If you need to create a case-sensitive vocabulary item, use a pattern vocabulary item. For more information, see Create a pattern vocabulary item.

주:

Choose a synonym that is a commonly-occurring word in the same language as your model.

In this example scenario, you are adding a vocabulary item for the word credentials. Say that your users may use credentials to refer to their password. By creating a vocabulary item, you can make sure that the system correctly predicts the intent for an utterance such as reset my credentials.

프로시저

1. Navigate to All > NLU Workbench > Models.
   The Virtual Agent tab opens by default.
2. Select the tab corresponding to your model's application, then select the name of your model.
3. On the Model details tab of the model overview, select the Vocabulary card.
4. In the Vocabulary tab, select Add a vocabulary.

   

5. In the Add a vocabulary window, select Regular as the Type.
6. Add a vocabulary word or phrase that your users might use, and then add the synonym that the model should use for intent prediction.

   In this example procedure, add credentials as the vocabulary and password as the synonym.

   

7. Select Save.

다음에 수행할 작업

To deploy your new vocabulary item, train and publish your model again.

Add more vocabulary items to improve model coverage and accuracy.