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In natural language processing, language identification or language guessing is the problem of determining which natural language given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods.
Each language is assigned a two-letter (set 1) and three-letter lowercase abbreviation (sets 2–5). [2] Part 1 of the standard, ISO 639-1 defines the two-letter codes, and Part 3 (2007), ISO 639-3 , defines the three-letter codes, aiming to cover all known natural languages , largely superseding the ISO 639-2 three-letter code standard.
Native-language identification (NLI) is the task of determining an author's native language (L1) based only on their writings in a second language (L2). [1] NLI works through identifying language-usage patterns that are common to specific L1 groups and then applying this knowledge to predict the native language of previously unseen texts.
Xerox, an online language identifier, 47 languages supported; Language Guesser, a statistical language identifier, 74 languages recognized; NTextCat - free Language Identification API for .NET (C#): 280+ languages available out of the box. Recognizes language and encoding (UTF-8, Windows-1252, Big5, etc.) of text. Mono compatible.
Language identification in the limit is a formal model for inductive inference of formal languages, mainly by computers (see machine learning and induction of regular languages). It was introduced by E. Mark Gold in a technical report [ 1 ] and a journal article [ 2 ] with the same title.
ISO 639 is a set of international standards that lists short codes for language names. The following is a complete list of three-letter codes defined in part two ( ISO 639-2 ) of the standard, [ 1 ] including the corresponding two-letter ( ISO 639-1 ) codes where they exist.
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Language identification; Natural-language understanding; Speech recognition This page was last edited on 1 July 2021, at 00:35 (UTC). Text is available under the ...