Question
Table of Contents
DRAG DROP (Drag & Drop is not supported)
Match the types of natural languages processing workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Workloads Types:
- Entity recognition
- Key phrase extraction
- Language modeling
- Sentiment analysis
- Natural language processing
- Translation
- Speech recognition and speech synthesis
Answer Area:
- Extracts persons, locations, and organizations from the text.
- Evaluates text along a positive-negative scale.
- Returns text translated to the specified target language.
Answer
- Entity recognition: Extracts persons, locations, and organizations from the text.
- Sentiment analysis: Evaluates text along a positive-negative scale.
- Translation: Returns text translated to the specified target language.
Explanation
Box 1: Entity recognition
Classify a broad range of entities in text, such as people, places, organisations, date/time and percentages, using named entity recognition. Whereas:- Get a list of relevant phrases that best describe the subject of each record using key phrase extraction.
Box 2: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 3: Translation
Using Microsoft’s Translator text API
This versatile API from Microsoft can be used for the following:
- Translate text from one language to another.
- Transliterate text from one script to another.
- Detecting language of the input text.
- Find alternate translations to specific text.
- Determine the sentence length.
Reference
- Microsoft Learn > Azure > Cognitive Services > Language Service > What is Named Entity Recognition (NER) in Azure Cognitive Service for Language?
- Text analytics
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