Microsoft Language Understanding (LUIS) - Short Review

Product Overview: Microsoft Language Understanding (LUIS)

Microsoft Language Understanding (LUIS), part of Azure Cognitive Services, is a powerful natural language processing (NLP) service designed to enable applications to understand and interpret user input in natural language. Here’s a detailed look at what LUIS does and its key features.

What LUIS Does

LUIS allows developers to build applications that can receive user input in natural language and extract meaningful information from it. This capability is crucial for creating conversational interfaces, chatbots, virtual assistants, and other AI-powered applications. By using machine learning, LUIS helps applications understand what a person wants in their own words, facilitating more intuitive and effective human-computer interactions.

Key Features and Functionality

Intent Recognition

LUIS is adept at recognizing the intent behind user queries or statements. For example, it can determine whether a user is asking for weather information, booking a flight, or making a restaurant reservation. Intents represent the user’s goal or purpose behind an utterance and are defined based on the actions or tasks that the application needs to perform in response to user requests.

Entity Recognition

In addition to intent recognition, LUIS can extract specific pieces of information from user input, known as entities. These entities can include dates, locations, product names, and other relevant details. Entity identification is crucial for providing accurate and context-specific responses to user queries.

Utterances

Utterances are the user inputs or statements that you want LUIS to understand. To train the LUIS model, you provide a set of example utterances for each intent and entity you want to recognize. These utterances help the model learn to map user inputs to the correct intents and entities.

Model Building and Training

LUIS offers two primary methods for building models: the Authoring APIs and the LUIS.ai web app. Both methods allow you and your collaborators to control the definition of your LUIS model, including managing models, versions, collaborators, external APIs, testing, and training. You can start with prebuilt domain models or build your own custom models tailored to your specific needs.

Active Learning

Once your application is published and real user utterances are entered, LUIS uses active learning to improve its performance. The system identifies utterances it is relatively unsure of and suggests them for review. By labeling these utterances and retraining the model, you can significantly enhance the system’s accuracy and performance over time.

Integration and Deployment

LUIS models can be deployed to an HTTP endpoint, allowing your client application to send user utterances and receive the evaluated results as JSON objects. This integration enables your application to take appropriate actions based on the user’s intent and extracted entities.

Scalability and Quota Management

For applications with high traffic, LUIS allows you to spread the load across multiple apps with the same app definition. You can create and assign multiple keys to the app and use Application Insights to monitor usage. This ensures that no single app is overwhelmed and helps in managing quota limits effectively.

Conclusion

Microsoft Language Understanding (LUIS) is a robust tool for developers looking to integrate natural language understanding into their applications. With its ability to recognize intents, extract entities, and continuously improve through active learning, LUIS simplifies the process of creating conversational and intelligent services. However, it is important to note that LUIS will be retired on October 1st, 2025, and new LUIS resources cannot be created after April 1st, 2023. Microsoft recommends migrating LUIS applications to conversational language understanding for continued support and multilingual capabilities.