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.
Highlights
The past few years have witnessed the increasing awareness on the importance of statistical methods in linguistics (Khany & Tazik, 2019; Nikitina & Furuoka, 2018; Norris et al, 2015)
The author tries to search the keywords related to machine learning (ML) in Web of Science with the list of linguistic journals ranked according to their impact
The results showed that random forests and k nearest neighbor algorithm outperformed other algorithms
Summary
Zhiqing Lin Faculty of English Language and Culture, Guangdong University of Foreign Studies, Guangzhou, China Correspondence: Zhiqing Lin, Faculty of English Language and Culture, Guangdong University of Foreign Studies, No 2 North Baiyun Avenue, Guangzhou, China
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
