Discover how Named Entity Recognition (NER), a key Natural Language Processing (NLP) feature, excels at categorizing unstructured text into predefined groups. Learn its applications and benefits for efficient text analysis.
Table of Contents
Question
Which Natural Language Processing (NLP) feature should you use to categorize words and phrases in unstructured text into multiple pre-made groups of categories?
A. Summarization
B. Question answering
C. Sentiment analysis
D. Named Entity Recognition (NER)
Answer
D. Named Entity Recognition (NER)
Explanation
You would use Named Entity Recognition (NER) to categorize words and phrases in unstructured text into multiple pre-made groups of categories. NER focuses on identifying and classifying specific entities within text into predefined categories. These categories can be:
- People (e.g., names)
- Organizations (e.g., companies, institutions)
- Locations (e.g., cities, countries)
- Dates and times (e.g., specific dates, time expressions)
- Custom categories defined by the user
Question answering is a feature that retrieves the most relevant answer from a given text based on a user’s question. It does not categorize words or phrases into predefined categories.
Summarization is a feature that aims to condense a text into a shorter version while retaining the main points. It does not categorize words or phrases into predefined groups.
Sentiment analysis aims to understand the general feeling or opinion expressed in a piece of text. It categorizes the overall sentiment as positive, negative, or neutral, and can be applied to various formats like reviews, social media posts, or articles to gauge the emotional tone of the content. It is not used for categorizing words or phrases into predefined groups.
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