Learn about the key types of natural language processing in Azure AI, including entity recognition, sentiment analysis, and translation. Discover how these powerful NLP techniques can extract insights from text data.
Table of Contents
Question
Match the types of natural languages processing workloads to the appropriate scenarios.
Types of natural languages processing:
- Entity recognition
- Key phrase extraction
- Translation
- Natural language processing
- Sentiment analysis
Scenario:
- Extracts persons, locations, and organizations from the text.
- Evaluates text along a positive-negative scale.
- Returns text translated to the specified target language.
Answer
Extracts persons, locations, and organizations from the text: Entity recognition
Evaluates text along a positive-negative scale: Sentiment analysis
Returns text translated to the specified target language: Translation
Explanation
The main types of natural language processing (NLP) covered in this question are:
- Entity recognition: This NLP technique extracts named entities like persons, locations, and organizations from unstructured text data. It allows you to identify and categorize the key entities mentioned.
- Sentiment analysis: Sentiment analysis evaluates a piece of text and determines if the emotional tone or sentiment is positive, negative, or neutral. This is useful for understanding opinions and attitudes expressed in data like customer reviews or social media posts.
- Translation: NLP translation takes text in one language and converts it to another specified target language. Azure Translator supports translation between dozens of languages while preserving the original meaning.
In summary, entity recognition pulls out specific named entities, sentiment analysis assesses the emotional tone, and translation converts between languages. Used together, these NLP techniques enable extracting valuable insights and intelligence from large volumes of unstructured text data.
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