What is Named Entity Recognition (NER) in Azure Cognitive Service for Language?
Named Entity Recognition (NER) is one of the features offered by Azure Cognitive Service for 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.
- 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.
To use this feature, you submit data 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.
Create an Azure Language resource, which grants you access to the features offered by Azure Cognitive Service for Language. It will generate a password (called a key) and an endpoint URL that you'll use to authenticate API requests.
Send the request containing your data as raw unstructured text. Your key and endpoint will be 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:
|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 Cognitive Services for Language:
|Development option / language||Reference documentation||Samples|
|REST API||REST API documentation|
|C#||C# documentation||C# samples|
|Java||Java documentation||Java Samples|
|Python||Python documentation||Python samples|
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 transparency note for NER to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
- Transparency note for Azure Cognitive Service for Language
- Integration and responsible use
- Data, privacy, and security
- 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.
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 Cognitive Service for 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.
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