This computer science problem involves algorithmic thinking and programming concepts. The solution below explains the approach, logic, and implementation step by step.

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Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language.
Applications: NLP is used in machine translation (e.g., Google Translate), sentiment analysis (determining emotional tone in text), spam detection, chatbots and virtual assistants (like Siri or Alexa), text summarization, and information extraction from large datasets.
Challenges: Key challenges include ambiguity (words having multiple meanings), contextual understanding (interpreting meaning based on surrounding text), sarcasm and irony detection, handling idioms and colloquialisms, dealing with grammatical errors and variations in language (dialects, slang), and the need for large, diverse datasets for training models.
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Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language.
This computer science problem involves algorithmic thinking and programming concepts. The solution below explains the approach, logic, and implementation step by step.