What is Named Entity Recognition (NER) in Azure AI Language?

Named Entity Recognition (NER) is one of the features offered by Azure AI Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. The NER feature can identify and categorize entities in unstructured text. For example: people, places, organizations, and quantities. The prebuilt NER feature has a preset list of recognized entities. The custom NER feature allows you to train the model to recognize specialized entities specific to your use case.

• Quickstarts are getting-started instructions to guide you through making requests to the service.
• How-to guides contain instructions for using the service in more specific or customized ways.
• The conceptual articles provide in-depth explanations of the service's functionality and features.

Typical workflow

To use this feature, you submit data for analysis and handle the API output in your application. Analysis is performed as-is, with no added customization to the model used on your data.

1. Create an Azure AI Language resource, which grants you access to the features offered by Azure AI Language. It generates a password (called a key) and an endpoint URL that you use to authenticate API requests.

2. Create a request using either the REST API or the client library for C#, Java, JavaScript, and Python. You can also send asynchronous calls with a batch request to combine API requests for multiple features into a single call.

3. Send the request containing your text data. Your key and endpoint are used for authentication.

4. Stream or store the response locally.

Get started with named entity recognition

To use named entity recognition, you submit raw unstructured text for analysis and handle the API output in your application. Analysis is performed as-is, with no additional customization to the model used on your data. There are two ways to use named entity recognition:

Reference documentation and code samples

As you use this feature in your applications, see the following reference documentation and samples for Azure AI Language:

Responsible AI

An AI system consists of more than just its core technology. It also includes the people who operate it, the people its use affects, and the broader deployment context. All these interconnected elements shape the effectiveness and outcomes of AI. Read the transparency note for NER to learn about responsible AI use and deployment in your systems. For more information, see the following articles:

• Transparency note for Azure AI Language
• Integration and responsible use
• Data, privacy, and security

Scenarios

• Enhance search capabilities and search indexing. Customers can build knowledge graphs based on entities detected in documents to enhance document search as tags.
• Automate business processes - Insurance claims, recognized entities like name and location can be highlighted to facilitate review. Support tickets can be automatically generated with customer name and company from an email.
• In-depth customer analysis. Determine the most popular information conveyed by customers in reviews, emails, and calls to determine relevant topics and trends over time.

Next steps

There are two ways to get started using the Named Entity Recognition (NER) feature:

• Azure AI Foundry is a web-based platform that lets you use several Language service features without needing to write code.
• The quickstart article for instructions on making requests to the service using the REST API and client library SDK.