Which NLP task is used to categorize text documents into predefined topics or categories based on content?

Which NLP task is used to categorize text documents into predefined topics or categories based on content?

• A. Text Classification
• B. Text Extraction
• C. Summarizing text
• D. Tone

Answer: (A)

Text classification is the process of assigning a document to predefined categories based on its content. This is exactly what you do when you categorize articles into topics like sports, politics, or technology—each document is analyzed and labeled with the topic it belongs to. Models learn from labeled examples, using features from the text (words, phrases, or semantic embeddings) to predict the most likely category. This focus on mapping text to predefined labels is what sets it apart from other tasks: text extraction pulls out specific pieces of information, summarizing creates a shorter version of the text, and tone targets sentiment or attitude rather than topic labels.

Text classification is the process of assigning a document to predefined categories based on its content. This is exactly what you do when you categorize articles into topics like sports, politics, or technology—each document is analyzed and labeled with the topic it belongs to. Models learn from labeled examples, using features from the text (words, phrases, or semantic embeddings) to predict the most likely category. This focus on mapping text to predefined labels is what sets it apart from other tasks: text extraction pulls out specific pieces of information, summarizing creates a shorter version of the text, and tone targets sentiment or attitude rather than topic labels.