Microsoft Certified: Azure AI Engineer AssociateImplement knowledge mining solutionsEasy
A research institution is building a knowledge mining solution to analyze scientific papers. They need to extract key terms and phrases from the abstracts and full text to improve search relevance. These terms are often domain-specific and not always single words. Which Azure AI Language feature, when integrated into an Azure AI Search skillset, is best suited for this task?
- ANamed Entity Recognition (NER)
- BSentiment Analysis
- CKey Phrase Extraction
- DLanguage Detection
Show answer & explanationAnswer & explanation
Correct answer: C. Key Phrase Extraction
Key Phrase Extraction is specifically designed to identify and extract the main concepts or key phrases from text, which is ideal for improving search relevance by highlighting important domain-specific terms.
Why the other options are wrong
- A. NER identifies predefined categories of entities (people, places, organizations), but it might not capture all 'domain-specific terms and phrases' that are not formal entities.
- B. Sentiment Analysis determines the emotional tone of text, not key terms.
- D. Language Detection identifies the language of the text, not key terms.
Key Phrase Extraction
An Azure AI Language feature that identifies and extracts the main concepts or topics from unstructured text.
- Outputs a list of key phrases.
- Useful for summarization, tagging, and improving search relevance.
- Can identify multi-word phrases.
Memory trick: To find the 'keys' to a paper, you need a 'key phrase extraction' tool to unlock the main ideas.