Natural Language Processing

 Natural Language Processing is the branch of AI that enables computers to understand, generate, and analyze human language in text or speech. It combines linguistics with machine learning to perform tasks such as tokenization, part-of-speech tagging, named-entity recognition, sentiment analysis, machine translation, and text summarization. Modern NLP pipelines use Transformer models—BERT, GPT-4, Llama 3—that learn context with self-attention and can be fine-tuned for domain jargon. Key metrics include BLEU for translation, F1 for entity extraction, and perplexity for language modeling. Applications span chatbots, voice assistants, search ranking, legal document review, and Retrieval-Augmented Generation (RAG) systems that ground LLM answers in curated sources. Challenges involve ambiguity, bias, and multilingual coverage, addressed by larger datasets, prompt engineering, and fairness audits. By turning raw language into structured data and coherent responses, Natural Language Processing powers today’s conversational and analytical AI.