Microsoft Azure AI Fundamentals AI-900 Practice Question
A company wants to develop a virtual assistant that can generate coherent and contextually appropriate responses to user queries by creating new sentences based on previous conversation.
Which type of natural language processing workload should they focus on to achieve this functionality?
Language Modeling enables systems to generate new text by predicting the next word in a sequence, allowing for the creation of contextually relevant and coherent responses. This is essential for applications like virtual assistants that need to produce human-like text.
Key phrase extraction, entity recognition, and sentiment analysis are focused on analyzing and extracting information from existing text rather than generating new content.
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What is Language Modeling in Natural Language Processing?
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How does Language Modeling differ from other NLP tasks like Key Phrase Extraction?
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Why are the other options like Key Phrase Extraction and Sentiment Analysis not suitable for generating coherent responses?
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Microsoft Azure AI Fundamentals AI-900
Describe Features of Natural Language Processing (NLP) Workloads on Azure
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