A Methodological Review of Machine Learning in Applied Linguistics

Autor/in	Lin, Zhiqing
Titel	A Methodological Review of Machine Learning in Applied Linguistics
Quelle	In: English Language Teaching, 14 (2021) 1, S.74-85 (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1916-4742
Schlagwörter	Applied Linguistics; Computer Software; Computational Linguistics; Interdisciplinary Approach; Comparative Analysis; Second Language Learning; Second Language Instruction; Research Reports; Phonetics; Neurolinguistics; Psycholinguistics; Language Tests; Clinical Diagnosis; Artificial Intelligence + Suchen Sie Ihr Suchwort? Linguistics; Linguistik; Angewandte Linguistik; Computerlinguistik; Fächerübergreifender Unterricht; Fächerverbindender Unterricht; Interdisziplinarität; Zweitsprachenerwerb; Fremdsprachenunterricht; Research report; Forschungsbericht; Phonetik; Fonetik; Neurolinguistisches Programmieren; Psycholinguistik; Language test; Sprachtest; Künstliche Intelligenz
Abstract	The traditional linear regression in applied linguistics (AL) suffers from the drawbacks arising from the strict assumptions namely: linearity, and normality, etc. More advanced methods are needed to overcome the shortcomings of the traditional method and grapple with intricate linguistic problems. However, there is no previous review on the applications of machine learning (ML) in AL, the introduction of interpretable ML, and related practical software. This paper addresses these gaps by reviewing the representative algorithms of ML in AL. The result shows that ML is applicable in AL and enjoys a promising future. It goes further to discuss the applications of interpretable ML for reporting the results in AL. Finally, it ends with the recommendations of the practical programming languages, software, and platforms to implement ML for researchers in AL to foster the interdisciplinary studies between AL and ML. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01