AI-900: Key Phrase Extraction in Natural Language Processing: Uncovering the Main Talking Points

Dive into the world of key phrase extraction in natural language processing. Discover how this technology identifies and extracts the most important phrases from text, enabling efficient information retrieval, summarization, and deeper understanding of content.

Question

You are developing a solution that uses the Text Analytics service. You need to identify the main talking points in a collection of documents. Which type of natural language processing should you use?

A. entity recognition
B. key phrase extraction
C. sentiment analysis
D. language detection

Answer

B. key phrase extraction

Explanation

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.

The correct answer is B. key phrase extraction.

Key phrase extraction is a type of natural language processing that can extract the most important or relevant words or phrases from a text, such as keywords, topics, or themes. Key phrase extraction can use advanced algorithms to analyze the frequency, position, or context of the words or phrases and return a list of key phrases.

You are developing a solution that uses the Text Analytics service. You need to identify the main talking points in a collection of documents. This is a scenario where key phrase extraction can be useful, as it can help you summarize the main ideas or points of the documents and understand what they are about.

The other three options are not types of natural language processing that can identify the main talking points in a collection of documents, but they can perform other tasks related to text analysis:

• Entity recognition is a type of natural language processing that can identify and label the names of specific things or concepts in a text, such as people, places, organizations, dates, or products. Entity recognition can use advanced algorithms to analyze the syntax, semantics, or morphology of the text and return a list of entities and their types.
• Sentiment analysis is a type of natural language processing that can determine how positive or negative a text is. Sentiment analysis can use advanced algorithms to assign a polarity score or a sentiment label to a text, such as happy, sad, angry, or neutral.
• Language detection is a type of natural language processing that can identify the language of a text, such as English, Spanish, or Chinese. Language detection can use advanced algorithms to compare the text with a set of known languages and return the most likely language or a list of possible languages.

Reference

Microsoft Learn > Azure > Architecture > Data Architecture Guide > Natural language processing technology

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