Build a Conversational Language Understanding Model (Part 2)

By Ashwin Venugopal -

 





Pre-Configured Features

The Azure AI Language service offers specific functionalities without requiring any model labelling or training. After you set up your resource, you can transmit your data and utilize the results returned in your application.

The following features are all pre-configured:

  • Summarization

Summarization is available for both documents and conversations, and will summarize the text into key sentences that are predicted to encapsulate the input's meaning.

  • Named Entity Recognition

Entity recognition can identify and extract various entities like individuals, locations, or organizations, enhancing your application's ability to understand different entity types for better natural language interaction. For instance, in the sentence "The waterfront pier is my favorite Seattle attraction," Seattle would be recognized and classified as a location.

  • Personally Identifiable Information (PII) Detection

PII detection allows you to identify, categorize, and redact information that could be considered sensitive, such as email addresses, home addresses, IP addresses, names, and protected health information. For example, if the text " email@contoso.com" was included in the query, the entire email address can be identified and redacted.

  • Key Phrase Extraction

Key phrase extraction is a capability that rapidly identifies the primary ideas from the text given. For example, with the sentence "Text Analytics is one of the features in Azure AI Services," the service would identify "Azure AI Services" and "Text Analytics."

  • Sentiment Analysis

Sentiment analysis determines whether a string or document is favorable or negative. Consider the following text: "Excellent hotel." The service would classify that as favorable with a comparatively high confidence score since it was "close to plenty of food and attractions we could walk to."

  • Language Detection

Language detection takes one or more documents, and identifies the language for each. For example, if the text of one of the documents was "Bonjour", the service would identify that as French.

  • Learned Features

To utilize learned features in your application, you must label data, train your model, and deploy it. You can alter the information that is extracted or anticipated thanks to these features.

Conclusion

We have successfully learnt about pre-configured features of Azure AI language.







Comments

Post a Comment

Popular posts from this blog

Information Protection Scanner: Resolve Issues with Information Protection Scanner Deployment

By Ashwin Venugopal -
Image
  About If you are experiencing problems with the Microsoft Purview Information Protection scanner, check the health of your deployment by utilizing the Start-ScannerDiagnosticsPowerShell cmdlet to start the scanner diagnostic tool. The diagnostics tool checks the following details and then creates a log files with the results: Whether the database is up to date. Whether network URLs are accessible. Whether there's a valid authentication token and policy can be acquired. Whether the profile is defined in the Azure portal. Whether offline/online configuration exists and can be acquired. Whether the rules configured are valid.  The Start-ScannerDiagnostics command does not perform a comprehensive check of prerequisites. If you encounter problems with the scanner, you need to verify that your system meets the scanner's requirements, and that your scanner's configuration and installation are finished.  Verify Scanning Details Per Scanner Node and Repository Run the Get-ScanSt...
Read more

Azure AI Search plugin in Microsoft Security Copilot (Preview)

By Ashwin Venugopal -
Image
  Introduction Azure AI Search is a service that allows to reliably extract content search, extract, and understand at scale. It is typically used to search for documents, data surveys, and chat-style Copilot apps over proprietary data. Indexing and querying are Azure AI Search's two main workloads. While querying allows to search the content from index, indexing loads the content into the search engine and makes it searchable. By connecting to one of the Azure AI Search indexes from within Copilot, the Microsoft Security Copilot integration for Azure enables to use the content in Copilot to produce contextualized, pertinent, and targeted responses.  Requirement Before using the Azure AI Search plugin in Copilot, the desired Azure AI Search index to connect to Copilot is configured as follows: The text field must be searchable.  The title field must be filterable. The vector-field must use text-embedding-ada-002. Integrated vectorization can be used to setup an index in t...
Read more

How AMI Store & Restore Works?

By Ashwin Venugopal -
Image
  CreateStoreImageTask The CreateStoreImageTask API stores an AMI as a single object in an S3 bucket. The API initiates a task that collects all the data from the AMI and its associated snapshots, subsequently using an S3 multipart upload to store this data in an S3 object. It gathers all elements of the AMI, including most of the non-Region-specific metadata and all EBS snapshots included in the AMI, and consolidates them into a single object in S3. During the upload process, the data is compressed to minimize the space utilized in S3, which means that the S3 object could be smaller than the total sizes of the snapshots in the AMI. If the account making this API call has access to AMI and snapshot tags, they will be retained. The S3 object shares the same ID as the AMI, except it has a .bin extension. Additionally, the S3 metadata tags for the object include the AMI name, AMI description, AMI registration date, AMI owner account, and a timestamp indicating when the store operation...
Read more