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Bridging the Native Language and Language Variety Identification Tasks
Author(s) -
Marc Franco-Salvador,
Greg Kondrak,
Paolo Rosso
Publication year - 2017
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2017.08.068
Subject(s) - computer science , variety (cybernetics) , bridging (networking) , language identification , natural language processing , identification (biology) , artificial intelligence , string (physics) , word (group theory) , task (project management) , language model , linguistics , natural language , computer network , philosophy , botany , physics , management , quantum mechanics , economics , biology
The objective of Native Language Identification is to determine the native language of the author of a text that he or she wrote in another language. By contrast, Language Variety Identification aims at classifying texts representing different varieties of a single language. We postulate that both tasks may be reduced to a single objective, which is to identify the language variety of the text. We design a general approach that combines string kernels and word embeddings, which capture different characteristics of texts. The results of our experiments show that the approach achieves excellent results on both tasks, without any task-specific adaptations.

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