Analyze Text With Azure AI Language

By Ashwin Venugopal -

 







Introduction

Language models are used by Natural Language Processing (NLP) systems to decipher spoken or written language's semantic meaning. You can create language models for your apps using the Language Understanding service.

The world produces enormous amounts of data every day, much of which is text-based and may be found in emails, social media posts, online reviews, corporate papers, and more. You can develop apps that derive meaning and insights from this text-based data by using artificial intelligence approaches that use statistical and semantic models.

The Azure AI Language provides an API for common text analysis techniques that you can easily integrate into your own application code.

Provision an Azure AI Language Resource

Azure AI Language is designed to help you extract information from text. It provides functionality that you can use for:

  • Language Detection - determining the language in which text is written.

  • Key Phrase Extraction - identifying important words and phrases in the text that indicate the main points.

  • Sentiment analysis - quantifying how positive or negative the text is.

  • Named Entity Recognition - detecting references to entities, including people, locations, time periods, organizations, and more.

  • Entity Linking - identifying specific entities by providing reference links to Wikipedia articles.

Azure Resources For Text Analysis

You must allocate a resource for Azure AI Language in your Azure subscription in order to use it for text analysis. Once a suitable resource has been provisioned in your Azure subscription, you can contact the Azure AI Language APIs from your code using its endpoint and one of its keys. The Azure AI Language APIs can be accessed via any of the available programming language-specific SDKs or by sending JSON-formatted requests to the REST interface.

Conclusion

We have successfully learnt the basics of Azure AI language resources.

 

 

 


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