Natural Language Processing (NLP)

Natural language processing encompasses all the techniques and technologies that allow computers to work with human language. NLP covers a broad spectrum of tasks including text classification, sentiment analysis, named entity recognition, machine translation, question answering, text summarization, and conversational AI. It is one of the oldest and most impactful subfields of artificial intelligence.

The history of NLP has evolved through several paradigms. Early systems relied on hand-crafted rules and grammars. Statistical NLP in the 1990s and 2000s used probabilistic models trained on annotated datasets. The deep learning revolution brought neural network approaches that learned features automatically from data. Today, large language models represent the latest paradigm, achieving state-of-the-art performance across virtually all NLP tasks with a single general-purpose model rather than requiring task-specific models.

Modern NLP powered by LLMs has made natural language the primary interface between humans and computers. Instead of learning programming languages or complex query syntax, users can now communicate with AI systems in plain language and receive intelligent responses. This has democratized access to AI capabilities and created new application categories including AI chatbots, voice assistants, content generation tools, and intelligent document processing systems.