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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).〔Wong, Sze-Meng Jojo, and Mark Dras. ("Exploiting parse structures for native language identification" ). Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011.〕 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. This is motivated in part by applications in second-language acquisition, Language teaching and forensic linguistics, amongst others. == Overview == NLI works under the assumption that an author's L1 will dispose them towards particular language production patterns in their L2, as influenced by their native language. This relates to cross-linguistic influence (CLI), a key topic in the field of second-language acquisition (SLA) that analyzes transfer effects from the L1 on later learned languages. Using large-scale English data, NLI methods achieve over 80% accuracy in predicting the native language of texts written by authors from 11 different L1 backgrounds. This can be compared to a baseline of 9% for choosing randomly. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Native-language identification」の詳細全文を読む スポンサード リンク
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