Language Transfer in Chinese EFL Learners’ Receptive Vocabulary Knowledge of Delexical and Lexical Verb+Noun Collocations
Bibliographic record
Abstract
Restricted verb+noun collocations in English comprise delexical verb+noun collocations and lexical verb+noun collocations. The former are combinations containing verbs with ‘light’ meaning, such as make a mistake, take pictures, and have dinner, while lexical verb+noun collocations refer to combinations with technical meaning or figurative sense, such as draw a conclusion and hold discussions. Many studies have shown that these collocations are challenging to non-native English speakers, but to what extent and why one type is more challenging than the other has not received much research attention. The current study focuses on Chinese EFL learners’ receptive knowledge of delexical and lexical verb+noun collocation, particularly in relation to the influence of their first language (L1). To address this, the study measured Chinese EFL learners’ receptive knowledge of delexical and lexical verb+noun collocations using COLLEX5 collocation test and the extent to which their responses in the test were congruent or incongruent with their L1, i.e., Mandarin. The results show that Chinese EFL learners’ receptive delexical verb+noun collocation knowledge is higher than their lexical ones. The results also show that 92.3% of delexical verb+noun collocation errors are congruent with Mandarin. L1 influence is also evident in lexical verb+noun collocation errors but to a lesser extent, i.e., 72.6%. These findings indicate that L1-influenced errors account for a significant portion of the errors, suggesting that EFL learners' L1 influences how L2 collocations are processed in the mental lexicon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".