Collecting collocations from general and specialised corpora
Bibliographic record
Abstract
Abstract Collocations are increasingly taken into account in general and specialised repositories and methodologies to collect them are heavily based on corpora. However, lexicographers and terminologists use different kinds of corpora in which combinations are likely to behave according to specific rules and/or patterns. This contribution presents a comparative analysis of the collocational behaviour of 15 lexical items found in a general language corpus and a specialised corpus on the theme of the environment. We automatically extracted large sets of collocates (three lists of 50 collocates) for each lexical item and from each corpus and analyse different facets of collocational behaviour: polysemy of lexical items, characteristics of collocates (overlap, rank and semantic classes of collocates, etc.). Our aim is to draw the attention of terminologists and lexicographers to some specific factors affecting the behaviour of collocations in specialized and general corpora.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".