Learn how to use key phrase extraction to identify the main talking points in a collection of documents.
Table of Contents
Question
You are developing a solution that needs to identify the main talking points in a collection of documents. Which type of natural language processing should you use?
A. Entity recognition
B. Sentiment analysis
C. Key phrase extraction
D. Language detection
Answer
C. Key phrase extraction
Explanation
Key phrase extraction evaluates unstructured text, and for each document returns a list of key phrases.
The correct answer is C. Key phrase extraction. This type of natural language processing is used to identify the main talking points or topics in a collection of documents. Key phrase extraction can help to summarize the content of a document, find relevant documents based on keywords, or analyze the themes and trends in a corpus of text.
Entity recognition is a type of natural language processing that identifies and categorizes named entities in a text, such as people, places, organizations, dates, etc. Entity recognition can help to extract information from a document, link entities to external sources, or perform tasks such as question answering or chatbot development.
Sentiment analysis is a type of natural language processing that determines the attitude or emotion of a speaker or writer towards a subject, such as positive, negative, or neutral. Sentiment analysis can help to understand the opinions and feedback of customers, users, or stakeholders, or monitor the social media sentiment of a brand, product, or topic.
Language detection is a type of natural language processing that identifies the language of a text, such as English, Spanish, Chinese, etc. Language detection can help to process multilingual documents, translate texts, or provide localized services.
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