Skip to Content

AI-900: What Are the Key NLP Features and Their Real-World Applications?

By: Author Alex Lim

Posted on

Categories Exam

Learn how to match key NLP features like Named Entity Recognition, Sentiment Analysis, and Summarization with their real-world applications for the AI-900 certification exam.

Table of Contents

Question

Match the Natural Language Processing (NLP) feature with its application.

Description:

  • Identifies and returns the main concepts in text.
  • Identifies and redacts sensitive information in text. Items such as email addresses, credit card information, passport numbers, and forms of identification are removed.
  • Analyzes sentiment (positive, negative, neutral) and opinions in text. This is commonly used by companies to see what consumers think of their products.
  • Categorizes items such as people, places, and dates in text.
  • Detects the language of a document. This feature returns a language code that can be used for various dialects or regional variations of a language.
  • Generates summaries of documents and conversations. Summarizes text by extracting sentences that represent the most relevant information about the content.

Feature:

  • Named Entity Recognition (NER)
  • Personally identifying (PII) and health (PHI) information detection
  • Language detection
  • Sentiment Analysis and opinion mining
  • Summarization

Answer

The correct matching of each Natural Language Processing (NLP) feature with its application is:

  • Named Entity Recognition (NER): Categorizes items such as people, places, and dates in text.
  • Personally identifying (PII) and health (PHI) information detection: Identifies and redacts sensitive information in text. Items such as email addresses, credit card information, passport numbers, and forms of identification are removed.
  • Language detection: Detects the language of a document. This feature returns a language code that can be used for various dialects or regional variations of a language.
  • Sentiment analysis and opinion mining: Analyzes sentiment (positive, negative, neutral) and opinions in text. This is commonly used by companies to see what consumers think of their products.
  • Summarization: Generates summaries of documents and conversations. Summarizes text by extracting sentences that represent the most relevant information about the content.
  • Key phrase extraction: Identifies and returns the main concepts in text.

Explanation

 

What Are the Key NLP Features and Their Real-World Applications?

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.