Qu'est-ce (qui) est string processing language - définition
FIELD OF COMPUTER SCIENCE AND LINGUISTICS
Natural Language Processing; The Natural Language Processing; Natural language and computation; Natural language recognition; Natual Language Processing; Statistical Natural Language Processing; Statistical natural-language processing; Computer natural language processing; Natural language processor; Natural language processors; NLP (computer science); Natural-language processing; Computer processing of natural language; Grammatical error correction; Grammatical error detection; Implicit semantic role labelling; Symbolic natural language processing; Statistical natural language processing
String PRocessING language
<language> (SPRING)
["From SPRING to SUMMER: Design, Definition and Implementation
of Programming Languages for String Manipulation and Pattern
Matching", Paul Klint, Math Centre, Amsterdam 1982 ].
(1996-02-06)
History of natural language processing
ASPECT OF HISTORY
History of NLP; History of Natural language processing; Machine learning algorithms for natural language processing
The history of natural language processing describes the advances of natural language processing (Outline of natural language processing). There is some overlap with the history of machine translation, the history of speech recognition, and the history of artificial intelligence.
DUP programming language
INTERPRETED AND FUNCTIONAL PROGRAMMING LANGUAGE.
DataUnit Processing language
DUP (DataUnit Processing language) is a special-purpose, interpreted and functional programming language. The DUP language looks like a mixture of C and ASN.
Wikipédia
Natural language processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.
Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.
