Natural language processing, or NLP, is the area of AI focused on working with human language. That includes reading, classifying, searching, extracting, translating, summarizing, generating, and conversing in text or speech. NLP is the bridge between machine computation and the messy, flexible way people actually communicate.
What NLP Covers
NLP includes many kinds of tasks. Some are analytical, such as sentiment analysis, document classification, entity extraction, and search. Others are generative, such as summarization, question answering, translation, and dialogue. Modern NLP also overlaps heavily with speech systems and multimodal interfaces that combine text with images or audio.
The field has evolved from rule-based and statistical methods into systems built on deep learning and Transformers. Today's large language models are a major part of NLP, but they are not the whole field. NLP also includes many smaller, targeted language systems.
Why NLP Matters
NLP matters because language is one of the main interfaces humans use to share knowledge and coordinate work. Better language systems make it easier to search information, automate documents, assist customers, analyze conversations, and build tools that feel more natural to use.
At the same time, language is full of ambiguity, context, tone, and cultural variation. That means NLP systems need careful evaluation, especially when they are used in high-stakes settings or across different populations and languages.
Related concepts: Large Language Model (LLM), Prompt, Prompt Engineering, Tokenization, and Multimodal Large Language Models.