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Microsoft Certified: Azure AI Engineer Associate - Certifications
Design and implement an Azure AI solution using Azure AI services, Azure AI Search, and Azure Open AI.
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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. The custom NER feature allows you to train the model to recognize specialized entities specific to your use case.
Note
Entity Resolution was upgraded to the Entity Metadata starting in API version 2023-04-15-preview. If you are calling the preview version of the API equal or newer than 2023-04-15-preview, please check out the Entity Metadata article to use the resolution feature.
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.
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. |
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 |
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:
There are two ways to get started using the Named Entity Recognition (NER) feature:
Training
Certification
Microsoft Certified: Azure AI Engineer Associate - Certifications
Design and implement an Azure AI solution using Azure AI services, Azure AI Search, and Azure Open AI.
Documentation
Custom named entity recognition - Azure AI services
Customize an AI model to label and extract information from documents using Azure AI services.
What is Azure AI Language - Azure AI services
Learn how to integrate AI into your applications that can extract information and understand written language.
How to perform Named Entity Recognition (NER) - Azure AI services
This article shows you how to extract named entities from text.