Struggling to extract key topics from text using Azure AI Language? Discover the preconfigured NLP feature that identifies phrases like “pasta” and “great service” for powerful text analysis. Learn how key phrase extraction works!
Table of Contents
Question
Which of the following is a preconfigured Natural Language Processing (NLP) feature in Azure AI Language that you can use to return the topics “pasta” and “great service” from the text, “The pasta was just amazing, and the service was great!”?
A. Text analytics for health
B. Named Entity Recognition (NER)
C. Key phrase extraction
D. Entity linking
Answer
C. Key phrase extraction
Explanation
Key phrase extraction is a preconfigured Natural Language Processing (NLP) feature in Azure AI Language that you can use to return the topics “pasta” and “great service” from the text, “The pasta was just amazing, and the service was great!”. Its primary function is to identify and return the most important and relevant thematic phrases from a given text. In the provided sentence, “pasta” and “great service” are the two prominent topics that summarize the key aspects of the experience (food and service). Key phrase extraction is designed to capture these essential themes, making it the most appropriate choice for this task.
Named Entity Recognition (NER) is a feature that focuses on identifying and classifying specific entities such as people, places, organizations, and dates, not general topics. While it may identify “pasta” as a food item, it would not necessarily consider it a topic.
Entity linking connects what? It connects the names of things you find in text (like “Washington”) to their corresponding entries in a knowledge base. Entity linking focuses on specific things mentioned in text, while topic extraction is about understanding the overall subject or theme of the text.
Text analytics for health is a specialized feature designed to extract and label medical information from healthcare documents, not general topics from everyday text.
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