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Another technique, as described by Cavnar and Trenkle (1994) and Dunning (1994) is to create a language n-gram model from a "training text" for each of the languages. These models can be based on characters (Cavnar and Trenkle) or encoded bytes (Dunning); in the latter, language identification and character encoding detection are integrated ...
The language makes a distinction between short and long vowels. ... Language Identification Web Service, language detection API, 100+ languages supported;
An IETF BCP 47 language tag is a standardized code that is used to identify human languages on the Internet. [1] The tag structure has been standardized by the Internet Engineering Task Force (IETF) [1] in Best Current Practice (BCP) 47; [1] the subtags are maintained by the IANA Language Subtag Registry.
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.
Character encoding detection, charset detection, or code page detection is the process of heuristically guessing the character encoding of a series of bytes that represent text. The technique is recognised to be unreliable [ 1 ] and is only used when specific metadata , such as a HTTP Content-Type: header is either not available, or is assumed ...
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The authors found that English remained at 45 percent of content for 2005 to the end of the study but believe this was due to the bias of search engines indexing more English-language content rather than a true stabilization of the percentage of content in English on the World Wide Web. [2] The number of non-English web pages is rapidly expanding.
As that same 2023 inspector general report also notes, all those legitimate Social Security numbers are floating around out there "hampers…efforts to prevent and detect fraud and misuse."