Automatic extraction of specialized verbal units
Bibliographic record
Abstract
Abstract This paper presents a methodology for the automatic extraction of specialized Arabic, English and French verbs of the field of computing. Since nominal terms are predominant in terminology, our interest is to explore to what extent verbs can also be part of a terminological analysis. Hence, our objective is to verify how an existing extraction tool will perform when it comes to specialized verbs in a given specialized domain. Furthermore, we want to investigate any particularities that a language can represent regarding verbal terms from the automatic extraction perspective. Our choice to operate on three different languages reflects our desire to see whether the chosen tool can perform better on one language compared to the others. Moreover, given that Arabic is a morphologically rich and complex language, we consider investigating the results yielded by the extraction tool. The extractor used for our experiment is TermoStat ( Drouin 2003 ). So far, our results show that the extraction of verbs of computing represents certain differences in terms of quality and particularities of these units in this specialized domain between the languages under question.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".