What is key phrase extraction in Azure Cognitive Service for Language?
Key phrase extraction 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. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff".
This documentation contains the following types of articles:
- 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.
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 entity linking
To use key phrase extraction, 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 key phrase extraction:
|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 key phrase extraction into your applications using the REST API, or the client library available in a variety of languages. For more information, see the key phrase extraction quickstart.|
|Docker container||Use the available Docker container to deploy this feature on-premises. These docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons.|
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 key phrase extraction 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
There are two ways to get started using the entity linking 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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