Extracting multiword expressions from texts with the aid of online resources
Bibliographic record
Abstract
Abstract This article reports on a classroom intervention where L2 learners were prompted to look for multiword expressions in texts. The participants were two intact classes of Vietnamese learners of English as a foreign language. Over a period of eight weeks, the experimental group (n = 26) looked for expressions in texts, while the comparison group (n = 28) used the same texts for content-related activities. In pairs, students in the experimental group consulted online dictionaries and an online corpus to help them determine which word strings in the texts were common expressions. The students’ worksheets and audio-recorded interactions suggest they were by and large successful at this, but also reveal the students found it hard to identify the boundaries of expressions and occasionally failed to find the dictionary (sub-)entries that matched them. The two groups’ ability to recall the expressions was gauged by comparing their scores on a pre-test and a post-test administered one week after the last class and again five months later. The learning gains were greater in the experimental group, although the difference fell short of significance in the delayed post-test. Students in the experimental group whose proficiency in English was relatively high tended to benefit the most.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".