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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.
The most likely language is the one with the model that is most similar to the model from the text needing to be identified. This approach can be problematic when the input text is in a language for which there is no model. In that case, the method may return another, "most similar" language as its result.
Identifying subdivisions of the Romance languages is inherently problematic, because most of the linguistic area is a dialect continuum, and in some cases political biases can come into play. A tree model is often used, but the selection of criteria results in different trees.
This is a list of languages by total number of speakers. It is difficult to define what constitutes a language as opposed to a dialect . For example, Chinese and Arabic are sometimes considered single languages, but each includes several mutually unintelligible varieties , and so they are sometimes considered language families instead.
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 .
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The theoretical foundations for what became known as the contrastive analysis hypothesis were formulated in Robert Lado's Linguistics Across Cultures (1957). In this book, Lado claimed that "those elements which are similar to [the learner's] native language will be simple for him, and those elements that are different will be difficult".
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