Natural Language Processing: From Text to Meaning

Abstract

Natural Language Processing (NLP) enables computers to understand and generate human language, powering applications like chatbots, sentiment analysis, and machine translation. Core techniques include tokenization, part-of-speech tagging, and named entity recognition, often implemented using libraries like NLTK or transformers like BERT.

Deep learning models, trained on massive text corpora, excel in tasks like question answering and summarization. Challenges include handling ambiguity, cultural nuances, and low-resource languages.

Future trends involve multimodal NLP (integrating text, images, and audio) and ethical considerations like bias mitigation.

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