A double blind peer reviewed online publication with in-print supplement since 2010    ISSN: 1949-260X

JTCLT Abstract

Volume 10 Number 1, 2019
Full issue PDF

Zhan, W., Cao, X., Cui, W., & Chang, B. (2019). An Experimental Study on Discriminating Chinese Near Synonyms: Contrasts between Machine Learning Systems and Second Language Learners. Journal of Technology and Chinese Language Teaching, 10(1), 1-25.
[詹卫东, 曹晓玉, 崔巍, & 常宝宝. (2019). 中文近义词辨析实验——机器学习程序与二语学习者的对比. 科技与中文教学 (Journal of Technology and Chinese Language Teaching), 10(1), 1-25.]

Full paper


In this article, machine learning technology is introduced into fill-in-the-blank (FITB) tasks involving the discrimination of Chinese near synonyms. A preliminary experimental study was carried out on said tasks between machines and L2 learners of Chinese. This study adheres to principles of balance and comprehensiveness in selecting synonyms and making test sets for the experimental research. The test results show that the performance of machines in discriminating Chinese near synonyms in FITB tasks is significantly comparable to that of human L2 learners. The score of the machine was also positively correlated with that of intermediate-level Chinese learners. In addition to the sets of near synonyms varying in difficulty, the difference of test question types also has a significant impact on test scores. The influence of lexical meaning features on the discrimination of near synonyms is no less than that of its formal features. Meanwhile, it is more effective for machines and L2 learners to exploit syntactic formal features rather than distinguishing collocation features in FITB tasks.


This website is supported by
Department of World Languages, Literatrues, and Cultures, Middle Tennessee State University
Page last updated: 2020-12-31