Language detection 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. Language detection is able to detect more than 100 languages in their primary script. In addition, it offers script detection to detect supported scripts for each detected language according to the ISO 15924 standard for a select number of languages supported by Azure AI Language Service.
This documentation contains the following types of articles:
Language detection: Returns one predominant language for each document you submit, along with its ISO 639-1 name, a human-readable name, confidence score, script name and script code according to ISO 15924 standard.
Script detection: To distinguish between multiple scripts used to write certain languages, such as Kazakh, language detection returns a script name and script code according to the ISO 15924 standard.
Ambiguous content handling: To help disambiguate language based on the input, you can specify an ISO 3166-1 alpha-2 country/region code. For example, the word "communication" is common to both English and French. Specifying the origin of the text as France can help the language detection model determine the correct language.
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 language detection, 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 three ways to use language detection:
Development option Description Azure AI Foundry Azure AI Foundry is a web-based platform that lets you use entity linking with text examples with your own data when you sign up. For more information, see the Azure AI Foundry website or Azure AI Foundry documentation. REST API or Client library (Azure SDK) Integrate language detection into your applications using the REST API, or the client library available in a variety of languages. For more information, see the language detection 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. Responsible AIAn 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's deployed. Read the transparency note for language detection to learn about responsible AI use and deployment in your systems. You can also see the following articles for more information:
Next stepsThere are two ways to get started using the entity linking feature:
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4