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Language identification

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Statistical approaches to language identification
– Statistical approaches use different techniques to classify data.
– Mutual information based distance measure compares the compressibility of text to known languages.
Language n-gram models can identify languages based on character or byte encoding.
– Řehůřek and Kolkus (2009) developed a method to detect multiple languages in short texts.
– Grefenstette proposed a statistical method based on the prevalence of certain function words.

Identifying similar languages
– Distinguishing between closely related languages is a challenge for language identification systems.
– The DSL shared task in 2014 provided a dataset for discriminating between similar languages.
– The best system in the DSL shared task achieved over 95% accuracy.
– Similar languages like Bulgarian and Macedonian or Indonesian and Malay have significant lexical and structural overlap.
– Zampieri et al. (2014) describe the results of the DSL shared task.

Language identification software
– Apache OpenNLP includes a statistical detector based on char n-grams and can distinguish 103 languages.
– Apache Tika contains a language detector for 18 languages.
– These software tools can be used for language identification tasks.
– The availability of language models and detectors in software simplifies the process of language identification.
Language identification software is an important tool for various applications.

Related fields and concepts
– Native Language Identification is a related field of study.
– Algorithmic information theory has connections to language identification.
– Artificial grammar learning is relevant to understanding language patterns.
– Family name affixes can provide insights into language identification.
– Kolmogorov complexity is a concept used in language identification research.

References
– Benedetto, Caglioti, and Loreto (2002) discuss language trees and zipping.
– Cavnar and Trenkle (1994) propose an n-gram-based text categorization method.
– Cilibrasi and Vitanyi (2005) explore clustering by compression for language identification.
– Dunning (1994) presents a statistical identification method for language.
– Goutte, Leger, and Carpuat (2014) describe the NRC system for discriminating similar languages.

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.

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