Two methods for extracting “specific” single-word terms from specialized corpora
Bibliographic record
Abstract
Recently, corpus comparison has been used by a number of researchers for extracting single-word terms (SWTs) from specialized corpora. It is viewed as a means to supplement multi-word term (MWT) extraction, the focus of which is on noun phrases. However, little is known about the value of this technique in a terminological setting. This paper examines two different methods for finding French SWTs in the field of computing. The first one (M1) compares the specialized corpus to a corpus considered to be a reflection of language as a whole. The second one (M2) breaks down the specialized corpus into six topical subcorpora that are compared in turn to the entire specialized corpus. The calculation relies on standard normal distribution and is carried out by a program calledTermoStat. The specific units produced by both methods are then evaluated by comparing them to the contents of two specialized dictionaries. We also compare the results yielded by the two methods. Results show that precision is fair (approximately 50%of units extracted by both methods can be found in specialized dictionaries). However, recall is lower in both methods. Results also reveal that, even though M1 yields better results that M2, both methods are useful for identifying SWTs and should be considered in terminological work.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".