Prenominal adjective order preferences in Chinese and German L2 English
A multifactorial corpus study
Bibliographic Data
| ID | 21201616 |
|---|---|
| Authors | Stefanie Wulff (0000-0003-3245-6064, University of Florida), Stefan Th Grie (0000-0002-6497-3958, University of California, Santa Barbara) |
| Year | 2015 |
| Volume | 5 |
| Issue | 1 |
| Pages | 122-150 |
| Publication date | 2015-05-07 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Linguistic Approaches to Bilingualism (JOURNAL) |
| Journal identifiers | ISSN: 1879-9264 • E-ISSN: 1879-9272 |
| Publisher | John Benjamins Publishing Company (PUBLISHER • NL) |
| DOI | 10.1075/lab.5.1.05wul |
| OpenAlex | W2027544142 |
| Language | EN |
| Citations received | 12 |
| References cited | 26 |
This study presents a contrastive analysis of 3624 instances of prenominal adjective order retrieved from the Chinese and German sections of the International Corpus of Learner English and the International Corpus of English . The data was annotated for nine determinants of adjective order, including semantic, frequency-related, and articulatory features. Applying a two-step regression procedure called MuPDAR ( Multifactorial Prediction and Deviation Analysis Using Regressions ), the present study finds that overall, the intermediate-advanced level learners are well-aligned with native speakers’ preferences. However, we also see that while the German learners seem generally better aligned with regard to frequency-related factors, the Chinese learners behave more target-like with regard to the effect of adjective gradability, and they seem more sensitive to segmental alternation constraints. In discussing these and other results, the study hopes to illustrate how corpus-based methods can make a valuable contribution to contemporary SLA research, specifically with regard to multifactorially determined phenomena such as adjective order
Adjective · German · Linguistics · Natural language processing · Noun · Computer Science · EFL/ESL Teaching and Learning · Natural Language Processing Techniques · Psychology · Second Language Acquisition and Learning · Artificial Intelligence
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Grammatical Categories
| Unique citing works | 12 |
|---|---|
| Citations per year | 1,2 |
| Citation span | 2016 - 2025 (10) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 12 |