A Corpus-Based Analysis of N-Grams in English Texts Written by Chinese Learners
Bibliographic record
Abstract
Recent linguistic studies have shown that proper use of multi-word expressions, or n-grams, is quite essential in foreign language acquisition. By contrast of the different types of n-grams from native corpus and Chinese learners’ corpus, the present article is devoted to discussing distinctive features of n-grams use in English texts written by Chinese learners. Generally, Chinese learners have not developed a strong awareness of distinguishing written style from spoken style of English texts. Also they tend to produce various n-grams which reflect the Chinese social development. Besides, they usually overuse n-grams of “a personal pronoun + a modal or mental verb” to stress human subjective ideas, attitudes or feelings toward the objective world. In addition, they can not output n-grams about quantity in a native-like way. The article also explores the causes of those peculiar features above mentioned from linguistic, social, cultural and cognitive perspectives. The study may not only helps readers get a better understanding about the n-gram features involved in Chinese learners’ English texts, but also brings some practical implications to EFL teaching.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".