Natural language processing, or NLP, is the field of AI focused on enabling computers to work with human language in text or speech form. This covers everything from a simple spell-checker to advanced systems that can summarise a lengthy report or hold an open-ended conversation.
Core Tasks Inside NLP
- Tokenization: breaking a sentence into smaller units such as words or subwords so a model can process it.
- Sentiment analysis: determining whether a piece of text expresses a positive, negative, or neutral tone.
- Named entity recognition: identifying names of people, places, organisations, and dates within text.
- Machine translation: converting text from one language into another while preserving meaning.
- Text generation: producing new, coherent text based on a prompt, which is what modern chat assistants do.
Why NLP Is Hard
Human language is full of ambiguity, sarcasm, slang, and context that changes meaning entirely. The same sentence can mean different things depending on tone or situation, and idioms rarely translate literally. Modern NLP systems handle much of this ambiguity far better than older rule-based systems because they learn statistical patterns of language use from enormous text collections rather than relying on rigid grammar rules.
NLP quietly powers search engines, customer support chatbots, voice assistants, translation apps, and grammar checkers. As language models have grown larger, NLP systems have become noticeably better at handling nuance, context, and multi-turn conversation.