To reduce false-positive intent predictions , add representative utterances to the None intent . The None intent exists specifically for user utterances that should not map to any defined business intent. Microsoft recommends adding examples that resemble potential false positives so that the model learns a stronger decision boundary between valid intent utterances and unrelated or ambiguous input.
For example, if the GetContactDetails intent contains requests such as "give me the customer phone number," the None intent should contain similar-looking but semantically unrelated phrases that could otherwise be incorrectly classified as GetContactDetails. During prediction, an utterance can be classified as None when it resembles None-training examples or when the highest intent score falls below the configured None threshold. Microsoft specifically advises adding false-positive examples to the None intent to improve intent discrimination.
Adding more examples to GetContactDetails alone generally strengthens recognition of that intent but does not provide the model with enough negative examples. A machine-learned entity addresses entity extraction rather than intent classification. Active learning helps identify uncertain utterances for review, but it is not the direct corrective action requested.
Study Guide references: Azure AI Language # Conversational Language Understanding; intents; None intent; intent classification; reducing false positives.