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 pre-set list of recognized entities.
- 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.
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.
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.
Send the request containing your text data. Your key and endpoint are used for authentication.
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:
| Development option | Description |
|---|---|
| Language studio | Language Studio is a web-based platform that lets you try entity linking with text examples without an Azure account, and your own data when you sign up. For more information, see the Language Studio website or language studio quickstart. |
| REST API or Client library (Azure SDK) | Integrate named entity recognition into your applications using the REST API, or the client library available in a variety of languages. For more information, see the named entity recognition quickstart. |
Reference documentation and code samples
As you use this feature in your applications, see the following reference documentation and samples for Azure AI Language:
| Development option / language | Reference documentation | Samples |
|---|---|---|
| REST API | REST API documentation | |
| C# | C# documentation | C# samples |
| Java | Java documentation | Java Samples |
| JavaScript | JavaScript documentation | JavaScript samples |
| Python | Python documentation | Python samples |
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 - For example, when reviewing insurance claims, recognized entities like name and location could be highlighted to facilitate the review. Or a support ticket could be generated with a customer's name and company automatically from an email.
- Customer analysis - Determine the most popular information conveyed by customers in reviews, emails, and calls to determine the most relevant topics that get brought up and determine trends over time.
Next steps
There are two ways to get started using the Named Entity Recognition (NER) feature:
- Language Studio, which is a web-based platform that enables you to try several Azure AI Language 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.