What is Azure Cognitive Service for Language?

Azure Cognitive Service for Language is a cloud-based service that provides Natural Language Processing (NLP) features for understanding and analyzing text. Use this service to help build intelligent applications using the web-based Language Studio, REST APIs, and client libraries.

This Language service unifies Text Analytics, QnA Maker, and LUIS and provides several new features as well. These features can either be:

  • Pre-configured, which means the AI models that the feature uses are not customizable. You just send your data, and use the feature's output in your applications.
  • Customizable, which means you'll train an AI model using our tools to fit your data specifically.

Migrate from Text Analytics, QnA Maker, or Language Understanding?

Azure Cognitive Services for Language unifies three individual language services in Cognitive Services - Text Analytics, QnA Maker, and Language Understanding (LUIS). If you have been using these three services, you can easily migrate to the new Azure Cognitive Services for Language. For instructions see Migrating to Azure Cognitive Services for Language.

Available features

Azure Cognitive Service for Language provides the following features:

Feature Description Deployment options
Named Entity Recognition (NER) This pre-configured feature identifies entities in text across several pre-defined categories. Language Studio
REST API and client-library
Personally Identifiable Information (PII) detection This pre-configured feature identifies entities in text across several pre-defined categories of sensitive information, such as account information. Language Studio
REST API and client-library
Key phrase extraction This pre-configured feature evaluates unstructured text, and for each input document, returns a list of key phrases and main points in the text. Language Studio
REST API and client-library
Docker container
Entity linking This pre-configured feature disambiguates the identity of an entity found in text and provides links to the entity on Wikipedia. Language Studio
REST API and client-library
Text Analytics for health This pre-configured feature extracts information from unstructured medical texts, such as clinical notes and doctor's notes. Language Studio
REST API and client-library
Docker container
Custom NER Build an AI model to extract custom entity categories, using unstructured text that you provide. Language Studio
REST API
Analyze sentiment and opinions This pre-configured feature provides sentiment labels (such as "negative", "neutral" and "positive") for sentences and documents. This feature can additionally provide granular information about the opinions related to words that appear in the text, such as the attributes of products or services. Language Studio
REST API and client-library
Docker container
Language detection This pre-configured feature evaluates text, and determines the language it was written in. It returns a language identifier and a score that indicates the strength of the analysis. Language Studio
REST API and client-library
Docker container
Custom text classification (preview) Build an AI model to classify unstructured text into custom classes that you define. Language Studio
REST API
Text Summarization (preview) This pre-configured feature extracts key sentences that collectively convey the essence of a document. Language Studio
REST API and client-library
Conversational language understanding (preview) Build an AI model to bring the ability to understand natural language into apps, bots, and IoT devices. Language Studio
Question answering This pre-configured feature provides answers to questions extracted from text input, using semi-structured content such as: FAQs, manuals, and documents. Language Studio
REST API and client-library

Tutorials

After you've had a chance to get started with the Language service, try our tutorials that show you how to solve various scenarios.

Additional code samples

You can find more code samples on GitHub for the following languages:

Deploy on premises using Docker containers

Use Language service containers to deploy API features on-premises. These Docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons. The Language service offers the following containers:

Responsible AI

An AI system includes not only the technology, but also the people who will use it, the people who will be affected by it, and the environment in which it is deployed. Read the following articles to learn about responsible AI use and deployment in your systems: