This study investigates the effects of teaching semantic radicals in inferring the meanings of unfamiliar characters among nonnative Chinese speakers. A total of 54 undergraduates majoring in Chinese Language from a university in Hanoi, Vietnam, who had 1 year of learning experience in Chinese were assigned to two experimental groups that received instructional intervention, called "old-for-new" semantic radical teaching, through two counterbalanced sets of semantic radicals, with one control group. All of the students completed pre- and post-tests of a sentence cloze task where they were required to choose an appropriate character that fit the sentence context among four options. The four options shared the same phonetic radicals but had different semantic radicals. The results showed that the pre-test and post-test score increases were significant for the experimental groups, but not for the control group. Most importantly, the experimental groups successfully transferred the semantic radical strategy to figure out the meanings of unfamiliar characters containing semantic radicals that had not been taught. The results demonstrate the effectiveness of teaching semantic radicals for lexical inference in sentence reading for nonnative speakers, and highlight the ability of transfer learning to acquire semantic categories of sub-lexical units (semantic radicals) in Chinese characters among foreign language learners.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660119 | PMC |
http://dx.doi.org/10.3389/fpsyg.2017.01846 | DOI Listing |
Brain Behav
January 2025
School of English Studies, Sichuan International Studies University, Chongqing, China.
Background: In Chinese phonogram processing studies, it is widely accepted that both character and non-character semantic radicals could be semantically activated. However, little attention was paid to the underlying workings that enabled the semantic radicals' semantic activation.
Purpose: The present study aimed to address the above issue by conducting two experiments.
Dyslexia
February 2025
Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
Med Image Anal
January 2025
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei Anhui, 230026, P.R. China; Center for Medical Imaging, Robotics, Analytic Computing & Learning(MIRACLE), Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, P.R. China; Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei Anhui, 230026, P.R. China; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS), Institute of Computing Technology, CAS, Beijing, 100190, P.R. China. Electronic address:
Q J Exp Psychol (Hove)
December 2024
Peabody College, Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA.
We hypothesised that people of different language backgrounds (English vs. Mandarin Chinese) might think about evolutionary relationships among living things differently. In particular, some reasoning heuristics may come from how living things are named.
View Article and Find Full Text PDFInvestig Clin Urol
November 2024
Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Purpose: Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!