Which of the following is a primary application of

Solution

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language. Text summarization is one of the most prominent applications of NLP, where the goal is to condense a large body of text into a shorter version that retains the key information. There are two main types of summarization: extractive (selecting key phrases directly from the text) and abstractive (generating new sentences to summarize the content). NLP techniques such as tokenization, part-of-speech tagging, and named entity recognition are often used in the process of text summarization. • Why this is correct: Text summarization is a core NLP task that aims to help users quickly digest large amounts of text data by providing concise summaries. Why Other Options Are Incorrect: 1. Image classification: This task belongs to computer vision, not NLP. 2. Predictive modeling for stock prices: This is a task for machine learning, specifically time series analysis, not NLP. 3. Feature selection in machine learning: Feature selection is a technique in ML, not specifically related to NLP. 4. Sentiment analysis of social media posts: While sentiment analysis is a task within NLP, it is more specific and focuses on understanding the emotional tone of the text, which is a different task from text summarization.