Introduction

  • 2 minutes

Natural language processing (NLP) is a common AI problem in which software must be able to work with text or speech in the natural language form that a human user would write or speak. Within the broader area of NLP, natural language understanding (NLU) deals with the problem of determining semantic meaning from natural language - usually by using a trained language model.

A common design pattern for a natural language understanding solution looks like this:

Diagram showing an app accepts natural language input, and uses a model to determine semantic meaning before taking the appropriate action.

In this design pattern:

  1. An app accepts natural language input from a user.
  2. A language model is used to determine semantic meaning (the user's intent).
  3. The app performs an appropriate action.

Azure AI Language enables developers to build apps based on language models that can be trained with a relatively small number of samples to discern a user's intended meaning.

In this module, you'll learn how to use the service to create a natural language understanding app using Azure AI Language.

After completing this module, you’ll be able to:

  • Provision an Azure AI Language resource.
  • Define intents, entities, and utterances.
  • Use patterns to differentiate similar utterances.
  • Use pre-built entity components.
  • Train, test, publish, and review a model.