Table of Contents
Question
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
API Features:
- Entity recognition
- Key phrase extraction
- Language detection
- Sentiment analysis
Answer Area:
- Understand how upset a customer is based on the text contained in the support ticket.
- Summarize important information from the support ticket.
- Extract key dates from the support ticket.
Answer
Sentiment analysis: Understand how upset a customer is based on the text contained in the support ticket.
Key phrase extraction: Summarize important information from the support ticket.
Entity recognition: Extract key dates from the support ticket.
Explanation
API Feature 1: Sentiment analysis. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
API Feature 2: Broad entity extraction: Broad entity extraction: Identify important concepts in text, including key. Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
API Feature 3: Entity Recognition: Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference
- Microsoft Learn > Azure > Architecture > Data Architecture Guide > Natural language processing technology
- Text analytics
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