AI-900: Enhancing Data Analysis: When to Use Key Phrase Extraction in Different Scenarios

Discover the optimal scenarios for utilizing key phrase extraction as a powerful data analysis tool. Explore its applications in translating documents, generating video captions, sentiment analysis of restaurant reviews, and identifying related documents. Unlock the potential of key phrase extraction to extract valuable insights and streamline various data-driven tasks.

Question

In which scenario should you use key phrase extraction?

A. translating a set of documents from English to German
B. generating captions for a video based on the audio track
C. identifying whether reviews of a restaurant are positive or negative
D. identifying which documents provide information about the same topics

Answer

D. identifying which documents provide information about the same topics

Explanation

The correct answer is D. identifying which documents provide information about the same topics.

Key phrase extraction is a feature of Azure AI Language that allows you to quickly identify the main concepts in text. For example, in the text “The food was delicious and there were wonderful staff”, key phrase extraction will return the main topics: “food” and “wonderful staff”.

This feature can be useful if you need to analyze a large collection of documents and find out which ones are related to the same topics. By extracting the key phrases from each document, you can compare them and group them based on their similarity. For example, you can use key phrase extraction to identify which news articles are about the same events, which research papers are about the same fields, or which product reviews are about the same features.

The other scenarios are not suitable for key phrase extraction, because they require different types of natural language processing features. For example:

• Translating a set of documents from English to German requires a language translation feature, which can convert text from one language to another while preserving the meaning and context.
• Generating captions for a video based on the audio track requires a speech-to-text feature, which can transcribe spoken words into written text, and a text summarization feature, which can condense long text into shorter sentences.
• Identifying whether reviews of a restaurant are positive or negative requires a sentiment analysis feature, which can evaluate the tone and emotion of text and assign a polarity score ranging from negative to positive.

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