Azure Cognitive Services: Language Understanding
Author: Ronald Fung
Creation Date: 31 May 2023
Next Modified Date: 31 May 2024
A. Introduction
Language Understanding (LUIS) is a cloud-based conversational AI service that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information. LUIS provides access through its custom portal, APIs and SDK client libraries.
For first time users, follow these steps to sign in to LUIS portal To get started, you can try a LUIS prebuilt domain app.
This documentation contains the following article types:
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.
Concepts provide in-depth explanations of the service functionality and features.
Tutorials are longer guides that show you how to use the service as a component in broader business solutions.
What does LUIS Offer
Simplicity: LUIS offloads you from the need of in-house AI expertise or any prior machine learning knowledge. With only a few clicks you can build your own conversational AI application. You can build your custom application by following one of our quickstarts, or you can use one of our prebuilt domain apps.
Security, Privacy and Compliance: LUIS is backed by Azure infrastructure, which offers enterprise-grade security, privacy, and compliance. Your data remains yours; you can delete your data at any time. Your data is encrypted while it’s in storage. Learn more about this here.
Integration: easily integrate your LUIS app with other Microsoft services like Microsoft Bot framework, QnA Maker, and Speech service.
B. How is it used at Seagen
As a biopharma research company that uses Microsoft Azure, you can use Azure Cognitive Services: Language Understanding (LUIS) to build natural language processing (NLP) applications that can understand user intent and respond appropriately. Here are some ways you can use Azure Cognitive Services: LUIS:
Drug information: You can use Azure Cognitive Services: LUIS to build a chatbot that can answer questions about drugs, such as their uses, side effects, and dosages.
Clinical trial matching: You can use Azure Cognitive Services: LUIS to build a clinical trial matching application that can understand user input and match them with relevant clinical trials.
Adverse event reporting: You can use Azure Cognitive Services: LUIS to build an adverse event reporting application that can understand user input and categorize adverse events based on severity and other criteria.
Patient feedback: You can use Azure Cognitive Services: LUIS to build a patient feedback application that can understand user input and provide appropriate responses based on the feedback.
Medical diagnosis: You can use Azure Cognitive Services: LUIS to build a medical diagnosis application that can understand user input and provide appropriate diagnoses based on symptoms and other criteria.
Overall, Azure Cognitive Services: LUIS provides a powerful and flexible tool for building natural language processing applications that can understand user intent and respond appropriately. By leveraging the machine learning and AI capabilities of the service, you can build applications that are customized to meet the unique needs of your research or business, and that enable you to more effectively engage with your customers or patients.
C. Features
Azure Cognitive Services: Language Understanding (LUIS) is a machine learning-based service that enables you to build natural language processing (NLP) applications that can understand user intent and respond appropriately. Here are some of the key features of Azure Cognitive Services: LUIS:
Intent recognition: Azure Cognitive Services: LUIS can recognize user intent based on natural language input, which allows you to build applications that can understand user input and respond appropriately.
Entity recognition: Azure Cognitive Services: LUIS can recognize entities within user input, which allows you to build applications that can understand the context of user input and provide more accurate responses.
Customization: Azure Cognitive Services: LUIS allows you to customize the recognition models to meet your specific needs, such as by adding custom entities or intents, and by configuring the service to recognize specific phrases or idioms.
Integration: Azure Cognitive Services: LUIS can be easily integrated with other Azure services, such as Azure Bot Service and Azure Functions, as well as third-party tools and services.
Training data: Azure Cognitive Services: LUIS requires high-quality training data to accurately recognize user intent and entities, which can be difficult to obtain and may require significant time and resources.
Language support: Azure Cognitive Services: LUIS supports a wide range of languages, which allows you to build applications that can understand user input in multiple languages.
Analytics: Azure Cognitive Services: LUIS provides analytics and insights into user input and application performance, which allows you to optimize and improve your applications over time.
Overall, Azure Cognitive Services: LUIS provides a powerful and flexible tool for building natural language processing applications that can understand user intent and respond appropriately. By leveraging the machine learning and AI capabilities of the service, you can build applications that are customized to meet the unique needs of your research or business, and that enable you to more effectively engage with your customers or patients.
D. Where Implemented
E. How it is tested
Testing Azure Cognitive Services: Language Understanding (LUIS) involves verifying that the service is accurately recognizing user intent and entities, that it is customized to meet your specific needs, and that it is providing appropriate responses to user input. Here are some steps you can take to test Azure Cognitive Services: LUIS:
Verify configuration: Verify that Azure Cognitive Services: LUIS is properly configured and integrated with your Azure account and resources.
Test intent recognition: Test Azure Cognitive Services: LUIS by submitting natural language input that contains known intents, and verifying that the service accurately recognizes the user intent.
Test entity recognition: Test Azure Cognitive Services: LUIS by submitting natural language input that contains known entities, and verifying that the service accurately recognizes the entities and provides appropriate responses.
Test customization: Test the customization capabilities of Azure Cognitive Services: LUIS by configuring the recognition models to meet your specific needs, and verifying that the service accurately recognizes the user intent and entities based on your unique requirements.
Test integration: Test the integration capabilities of Azure Cognitive Services: LUIS by integrating it with other Azure services or third-party tools, and verifying that the service works seamlessly with your existing workflows and platforms.
Test training data: Test the training data of Azure Cognitive Services: LUIS by submitting natural language input that is similar to your training data, and verifying that the service accurately recognizes the user intent and entities based on the training data.
Test language support: Test the language support capabilities of Azure Cognitive Services: LUIS by submitting natural language input in languages other than English, and verifying that the service accurately recognizes the user intent and entities in those languages.
Overall, testing Azure Cognitive Services: LUIS involves verifying that the service is properly configured and functioning as expected, testing intent recognition, entity recognition, customization, integration, training data, and language support. By testing Azure Cognitive Services: LUIS, you can ensure that you are effectively using the service to build natural language processing applications that can understand user intent and respond appropriately, and that you are benefiting from the accuracy, flexibility, and scalability it provides.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
As with any software or service, there may be known issues or limitations that users should be aware of when using Azure Cognitive Services: Language Understanding (LUIS). Here are some of the known issues for Azure Cognitive Services: LUIS:
Limited accuracy: While Azure Cognitive Services: LUIS provides accurate results in many cases, it may not always accurately recognize user intent and entities, particularly in cases where the input is complex or contains subtle nuances.
Limited customization: Azure Cognitive Services: LUIS has limited customization options, which can limit the ability of users to configure the service to their specific needs.
Limited training data: Azure Cognitive Services: LUIS requires a large amount of high-quality training data to accurately recognize user intent and entities, which can be difficult to obtain and may require significant time and resources.
Limited integration: Azure Cognitive Services: LUIS has limited integration with third-party tools and services, which can limit the ability of users to incorporate it into their existing workflows.
Cost: Azure Cognitive Services: LUIS can be expensive for users with limited budgets, particularly if they use it frequently or for large volumes of data.
Limited language support: Azure Cognitive Services: LUIS has limited language support, which can limit its usefulness for users who work with natural language input in languages other than English.
Overall, while Azure Cognitive Services: LUIS offers a powerful and flexible tool for building natural language processing applications that can understand user intent and respond appropriately, users must be aware of these known issues and take steps to mitigate their impact. This may include carefully configuring the service to meet the specific needs of their data, carefully monitoring the accuracy of the service to ensure that it is a good fit for their data requirements, and carefully integrating the service into their existing workflows to ensure that it is effectively utilized. By taking these steps, users can ensure that they are effectively using Azure Cognitive Services: LUIS to build natural language processing applications that can understand user intent and respond appropriately, and that they are benefiting from the accuracy, flexibility, and scalability it provides.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture