Natural language processing

NLPnatural languagenatural-language processingNatural Language Processing (NLP)Natural Language ProcessorLanguagelinguistic softwareMedical Language ProcessingNatural language and computationnatural language processors
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.wikipedia
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Artificial intelligence

AIA.I.artificially intelligent
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.

Natural-language understanding

natural language understandinglanguage understandingUnderstanding
Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.
Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension.

History of natural language processing

The history of natural language processing (NLP) generally started in the 1950s, although work can be found from earlier periods.
The history of natural language processing describes the advances of natural language processing (Outline of natural language processing).

Information engineering (field)

information engineeringInformationIE/Information engineering
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
The components of information engineering include more theoretical fields such as machine learning, artificial intelligence, control theory, signal processing, and information theory, and more applied fields such as computer vision, natural language processing, bioinformatics, medical image computing, cheminformatics, autonomous robotics, mobile robotics, and telecommunications.

ELIZA

DOCTORdoctor.elElisa
Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966.
ELIZA is an early natural language processing computer program created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.

Chatbot

chatterbotchatbotschatterbots
During this time, many chatterbots were written including PARRY, Racter, and Jabberwacky.
Some chatbots use sophisticated natural language processing systems, but many simpler ones scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database.

Turing test

Imitation GameTuringsounds as if it has been written by a person
In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence.
To pass a well-designed Turing test, the machine must use natural language, reason, have knowledge and learn.

Cache language model

The cache language models upon which many speech recognition systems now rely are examples of such statistical models.
These occur in the natural language processing subfield of computer science and assign probabilities to given sequences of words by means of a probability distribution.

Ontology (information science)

ontologyontologiesOntology (computer science)
During the 1970s, many programmers began to write "conceptual ontologies", which structured real-world information into computer-understandable data.
Artificial intelligence has retained the most attention regarding applied ontology in subfields like natural language processing within machine translation and knowledge representation, but ontology editors are being used often in a range of fields like education without the intent to contribute to AI.

Deep learning

deep neural networkdeep neural networksdeep-learning
In the 2010s, representation learning and deep neural network-style machine learning methods became widespread in natural language processing, due in part to a flurry of results showing that such techniques can achieve state-of-the-art results in many natural language tasks, for example in language modeling,
Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

Word embedding

word embeddingsembeddingsWord vectors
Popular techniques include the use of word embeddings to capture semantic properties of words, and an increase in end-to-end learning of a higher-level task (e.g., question answering) instead of relying on a pipeline of separate intermediate tasks (e.g., part-of-speech tagging and dependency parsing).
Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Joseph Weizenbaum

computer languagesProfessor Joseph Weizenbaum
Some notably successful natural language processing systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966.
In 1966, he published a comparatively simple program called ELIZA, named after the ingenue in George Bernard Shaw's Pygmalion, which performed natural language processing.

Machine learning

machine-learninglearningstatistical learning
Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of machine learning algorithms for language processing.
Inductive logic programming is particularly useful in bioinformatics and natural language processing.

Linguistics

linguistlinguisticlinguists
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
Linguistic research is commonly applied to areas such as language education, lexicography, translation, language planning, which involves governmental policy implementation related to language use, and natural language processing.

Sentence boundary disambiguation

sentence segmentationSentence breakingsentence splitter
Sentence boundary disambiguation (SBD), also known as sentence breaking, sentence boundary detection, and sentence segmentation, is the problem in natural language processing of deciding where sentences begin and end.

Computer science

computer scientistcomputer sciencescomputer scientists
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Speech recognition

voice recognitionautomatic speech recognitionvoice command
Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. The cache language models upon which many speech recognition systems now rely are examples of such statistical models.
Conferences in the field of natural language processing, such as ACL, NAACL, EMNLP, and HLT, are beginning to include papers on speech processing.

Machine translation

translation softwareautomatic translationtranslation
The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English.
In NLP, ontologies can be used as a source of knowledge for machine translation systems.

Parsing

parserparseparsed
In some machine translation and natural language processing systems, written texts in human languages are parsed by computer programs.

Stochastic grammar

statistical grammars
Statistical natural language processing uses stochastic, probabilistic and statistical methods, especially to resolve difficulties that arise because longer sentences are highly ambiguous when processed with realistic grammars, yielding thousands or millions of possible analyses.

Feature learning

representation learningefficient codingsefficient data codings
In the 2010s, representation learning and deep neural network-style machine learning methods became widespread in natural language processing, due in part to a flurry of results showing that such techniques can achieve state-of-the-art results in many natural language tasks, for example in language modeling,
K-means also improves performance in the domain of NLP, specifically for named-entity recognition; there, it competes with Brown clustering, as well as with distributed word representations (also known as neural word embeddings).

Probabilistic context-free grammar

stochastic context-free grammarSCFGsWeighted context-free grammar
PCFGs have application in areas as diverse as natural language processing to the study the structure of RNA molecules and design of programming languages.

Question answering

answer enginequestion answering systemquestion-answering
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language.

Sentiment analysis

sentimentopinion mininganalysis
Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

Probability

probabilisticprobabilitieschance
However, part-of-speech tagging introduced the use of hidden Markov models to natural language processing, and increasingly, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data.
The cache language model and other statistical language models that are used in natural language processing are also examples of applications of probability theory.