What is Natural Language Processing (NLP)?
NLP is the field of AI focused on making software understand and work with human language — classifying, extracting, translating, and summarising it.
Natural language processing is the older, broader discipline that language models now dominate. It covers every task where software handles human language: detecting sentiment in reviews, extracting names and amounts from invoices, tagging support tickets by topic, checking grammar, translating, and summarising. Classic NLP used hand-built rules and statistical models trained for one narrow job, which made each task accurate but rigid. Modern NLP mostly means pointing a general language model at the task with instructions, which is dramatically faster to build and flexible enough to change with a sentence. The trade-off is predictability: a purpose-built classifier gives consistent, measurable accuracy, while a general model may vary between runs. In tool comparisons, NLP is where the boring, high-value automation lives — routing, extraction, and tagging save more hours than content generation for most operations teams. Judge these features on measured accuracy against your own sample data, not on demos using tidy examples.
Also known as
- natural language processing
- text analytics
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Related terms
- Large Language Model (LLM) — An LLM is an AI model trained on huge amounts of text that predicts language well enough to write, summarise, translate, and answer questions.
- Generative AI — Generative AI is any AI that creates new content — text, images, audio, video, or code — rather than only analysing existing data.
- Vector Database — A vector database stores text or images as numerical embeddings so software can find results by meaning rather than exact keywords.
- Multimodal AI — Multimodal AI describes models that understand or produce more than one type of content — text, images, audio, and video together.
Category: AI Concepts