Can Word-Word Space Facilitate L2 Chinese Reading
Evidence From the Two Empirical Studies by Advanced L2 Learners of Mandarin Chinese
Bibliographic Data
| ID | 4881650 |
|---|---|
| Authors | Ken Chen (0000-0002-9118-7469, Shanghai International Studies University, corresponding author), Laiyuan Gu (0000-0002-0377-4939, Chinese University of Hong Kong, corresponding author), Lei Gu (0000-0001-5119-3517), Hongshan Zuo (0000-0002-9089-8752, Sichuan University), Qiaoyan Bai (Southwestern University of Finance and Economics) |
| Year | 2021 |
| Volume | 11 |
| Issue | 4 |
| Publication date | 2021-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | SAGE Open (JOURNAL) |
| Journal identifiers | ISSN: 2158-2440 • E-ISSN: 2158-2440 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/21582440211059150 |
| OpenAlex | W3217211658 |
| Language | EN |
| Citations received | 3 |
| References cited | 25 |
The purpose of this article aims to analyze the effect of word-word space in written Chinese to advanced non-native speakers when they read and process Mandarin texts. The participants have performed one online reaction time experiment and another one offline pencil-paper test. The results indicate that the structure of word segmentation in written Chinese texts have play an effective role in sentences' semantic processing, and the length and difficulty of sentences stimuli have also displayed significant function for their Chinese sentences processing. However, the results of offline test show that the combinational amount of segmental words have not affected the texts materials processed by advanced L2 participants. These results suggest that word boundary can facilitate L2 learners of Mandarin Chinese in processing text during their reading. Apart from theoretical implications, this article also proposes a new pedagogical approach to teaching text segmentation in Chinese, which can be useful in instructing Chinese as a second or foreign language
Chinese as a foreign language · Linguistics · Mandarin Chinese · Natural language processing · Segmentation · Text segmentation · Word processing · Computer Science · Natural Language Processing Techniques · Psychology · Second Language Acquisition and Learning · Text Readability and Simplification · Artificial Intelligence
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| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |