natural language processing
In plain wordsThe field that teaches computers to handle human language — to read, interpret, translate, summarize and produce text and speech. It is one of the oldest and largest areas of artificial intelligence.
The field concerned with computational methods for analyzing, understanding and generating human (natural) languages, as opposed to programming or other formal languages. Its classic tasks include machine translationmachine translation, n.: Software that renders writing or speech in one language into another language, as online translation services and phone translation apps do., question answeringquestion answering, n.: Systems that reply to people's questions with the right answer, whether by finding it in a passage they are given, searching a database or the web, or drawing on what they already know., text summarizationtext summarization, n.: Using a computer to shorten a document, meeting or set of articles into a brief version that keeps the main points., text classificationtext classification, n.: Sorting pieces of text into categories automatically — what topic an article covers, whether a message is spam, which team should handle a customer's complaint., sentiment analysissentiment analysis, n.: Using software to judge the feelings or opinions expressed in text — whether a review, post or comment is positive, negative or neutral, and about what., named-entity recognitionnamed-entity recognition, n.: Software that spots names and similar expressions in text and labels each by type: in 'Maria flew from Lagos to Paris on Monday', it marks a person, two cities and a date., part-of-speech taggingpart-of-speech tagging, n.: Labeling each word in a sentence with its grammatical role — noun, verb, adjective and so on — as in 'Time (noun) flies (verb) like (preposition) an arrow'., syntactic parsingsyntactic parsing, n.: Working out the grammatical structure of a sentence — which words group together and which word modifies which — usually drawn as a tree. and information extractioninformation extraction, n.: Automatically pulling structured facts out of ordinary text — who did what, when, where and for how much — and putting them into a table or database.; it is often divided into natural language understandingnatural language understanding, n.: The part of language AI concerned with working out what a piece of text means — who did what to whom, what the writer wants and what follows from it — rather than with producing text. and natural language generationnatural language generation, n.: Getting computers to produce human language — from turning a spreadsheet into a weather report to writing an essay or a chatbot's reply., and overlaps with computational linguisticscomputational linguistics, n.: The science of human language studied with computers: using computational models to understand how language works, and building the language technology that grows out of that understanding., information retrievalinformation retrieval, n.: The study and engineering of search: how to organize huge piles of documents so that the few that matter to someone's question can be found and ranked quickly. AI systems rely on it to look things up. and speech recognitionspeech recognition, n.: Technology that turns spoken words into written text, as when a phone transcribes a voice message or captions appear automatically under a video.. Methods have passed through three broad eras: hand-written rules and grammars (1950s–1980s); statistical and machine-learning models trained on annotated corporacorpus, n.: A large collection of written or spoken language, gathered so that people or computers can study it or learn from it — like a library assembled for a language model. (late 1980s–2010s); and neural networks, culminating in pretrained transformerstransformer, n.: The design behind today's chatbots and much of modern AI. It handles text by letting every word look at every other word and decide which of them matter for understanding it. and large language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code. that handle most tasks with one prompted model rather than separate task-specific systems. That shift has moved much of the field's attention from building pipelines to evaluating, steering and explaining general models.
Ten years ago our NLP stack had a tagger, a parser and an entity linker; now it is one model call.
Origin
The field's start is usually dated to Warren Weaver's 1949 memorandum on machine translation and the 1954 Georgetown–IBM demonstration, which translated more than 60 Russian sentences. The earliest use of the phrase 'natural language processing' is not established; it was in use for conferences and books by the early 1970s and later became the umbrella term for language technology.
Usage note
Older usage often excluded speech, which belonged to speech processing; today 'NLP' frequently covers both. Unrelated to neuro-linguistic programming, a self-help method that shares the abbreviation.
Related
- computational linguisticscomputational linguistics, n.: The science of human language studied with computers: using computational models to understand how language works, and building the language technology that grows out of that understanding.
- natural language understandingnatural language understanding, n.: The part of language AI concerned with working out what a piece of text means — who did what to whom, what the writer wants and what follows from it — rather than with producing text.
- natural language generationnatural language generation, n.: Getting computers to produce human language — from turning a spreadsheet into a weather report to writing an essay or a chatbot's reply.
- large language modellarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code.
- machine translationmachine translation, n.: Software that renders writing or speech in one language into another language, as online translation services and phone translation apps do.
- language modellanguage model, n.: A system that has learned how likely different sequences of words are, so it can predict the next word in a text and, by doing that repeatedly, write text of its own.
- corpuscorpus, n.: A large collection of written or spoken language, gathered so that people or computers can study it or learn from it — like a library assembled for a language model.
Sources
- Jurafsky, D. & Martin, J. H. (2025). Speech and Language Processing, 3rd ed. (draft). Stanford University.
Mentioned in
artificial intelligenceartificial intelligence, n.: Computer systems that do things we would normally credit to a thinking mind, such as holding a conversation, reading an X-ray or planning a route, and the field of research that builds them., large language modellarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code., AI-completeAI-complete, adj.: Describes a problem believed to be so hard that solving it would require general, human-level intelligence, such as flawless translation or truly understanding a story., Association for Computational LinguisticsAssociation for Computational Linguistics, prop. n.: The scientific society at the center of research on language technology, from translation software to chatbots. It runs the field's leading conferences and a free online archive of their papers., computational linguisticscomputational linguistics, n.: The science of human language studied with computers: using computational models to understand how language works, and building the language technology that grows out of that understanding., conversational AIconversational AI, n.: The technology that lets computers hold conversations with people in everyday language, by text or voice. It powers chatbots, voice assistants and automated customer-service lines., corpuscorpus, n.: A large collection of written or spoken language, gathered so that people or computers can study it or learn from it — like a library assembled for a language model., document understandingdocument understanding, n.: Using AI to read documents such as invoices, forms, contracts and scientific papers the way a person does, taking in layout, tables and charts as well as words, to pull out the information needed., Emily M. BenderEmily M. Bender, prop. n.: An American linguist who argues that language models only mimic the form of language without understanding it, and who co-coined the phrase 'stochastic parrots'., information extractioninformation extraction, n.: Automatically pulling structured facts out of ordinary text — who did what, when, where and for how much — and putting them into a table or database., latent Dirichlet allocationlatent Dirichlet allocation, n.: A popular topic-finding method that assumes every document is a blend of a few themes and every theme favors certain words, then works backward from the words to find both., low-resource languagelow-resource language, n.: A language with little digital text, recorded speech or language tools available — often one spoken by millions — which makes AI systems much weaker in it than in English., machine translationmachine translation, n.: Software that renders writing or speech in one language into another language, as online translation services and phone translation apps do., named-entity recognitionnamed-entity recognition, n.: Software that spots names and similar expressions in text and labels each by type: in 'Maria flew from Lagos to Paris on Monday', it marks a person, two cities and a date., natural language generationnatural language generation, n.: Getting computers to produce human language — from turning a spreadsheet into a weather report to writing an essay or a chatbot's reply., natural language understandingnatural language understanding, n.: The part of language AI concerned with working out what a piece of text means — who did what to whom, what the writer wants and what follows from it — rather than with producing text., sentiment analysissentiment analysis, n.: Using software to judge the feelings or opinions expressed in text — whether a review, post or comment is positive, negative or neutral, and about what., topic modeltopic model, n.: A method that reads a large pile of documents and discovers the themes running through them, describing each theme by its typical words and each document by its mix of themes., ALPAC reportALPAC report, prop. n.: A 1966 US government report that concluded machine translation was slower, worse and costlier than human translators. It ended most American funding for the field for many years., Brown CorpusBrown Corpus, prop. n.: The first large collection of English text stored on computer for research: about a million words from 500 American publications of 1961, assembled at Brown University in the 1960s., cognitive computingcognitive computing, n.: A label, promoted heavily by IBM in the 2010s, for computer systems that learn, reason and interact with people in humanlike ways. It was largely a marketing term and has given way to 'AI'., COLMCOLM, prop. n.: An academic conference, founded in 2024, devoted entirely to research on language models, the technology behind AI chatbots., EMNLPEMNLP, prop. n.: A leading yearly conference on getting computers to handle human language, founded in the 1990s for data-driven methods and now dominated by work on large language models., Georgetown–IBM experimentGeorgetown–IBM experiment, prop. n.: A 1954 public demonstration in New York in which an IBM computer translated about sixty Russian sentences into English. It raised hopes for automatic translation that took decades to fulfill.