Language Understanding (LUIS)

Language Understanding (LUIS) is a cloud-based service that applies custom natural language understanding models. LUIS enables you to build applications that can understand what users want by analyzing their utterances. It extracts intent and entities from natural language text.

LUIS allows you to create custom models to understand the specific language of your users, no matter the domain. This makes it ideal for building conversational interfaces, chatbots, and other AI-powered applications.

Get Started with LUIS

What is LUIS?

Language Understanding (LUIS) is a part of Azure Cognitive Services that allows developers to create custom natural language understanding (NLU) models. It helps applications understand user input by identifying intents and extracting key information (entities).

By training LUIS with example utterances, you can build a powerful NLU engine tailored to your specific application's needs.

Key Concepts

Utterances

Utterances are the sample phrases users might say or type to your application. The more diverse and representative your utterances are, the better your LUIS model will perform.

Example: "Book a flight to London for me and my family tomorrow."

Intents

An intent represents an action or a goal a user wants to achieve. You define intents that your application should recognize.

Example Intents: BookFlight, CancelOrder, GetDirections.

Entities

Entities are the specific pieces of information within an utterance that LUIS extracts. They are used to parameterize intents or provide data for your application.

Example Entities for BookFlight intent:

Domains

LUIS offers pre-built domains that provide starter models for common scenarios, such as "Home Automation," "Conversational Business," or "Order Status." These can significantly speed up development.

How LUIS Works

  1. Create an app: Define intents and entities relevant to your application.
  2. Add utterances: Provide example phrases for each intent.
  3. Label entities: Tag specific words or phrases in utterances as entities.
  4. Train the model: LUIS uses your data to train a machine learning model.
  5. Test and improve: Evaluate the model's performance and iteratively add more data or refine existing definitions.
  6. Publish: Make your trained model available via an endpoint to be consumed by your application.

Tutorials and Quickstarts

Dive into building your first LUIS application with our comprehensive guides:

SDK and API Reference

Integrate LUIS into your applications using our robust SDKs and REST APIs.

Pricing and Limits

Explore the pricing tiers for LUIS and understand the usage limits associated with each plan.

View LUIS pricing details

Support and Community

Get help and connect with other developers: