Construction d'un dictionnaire : morphologie à deux niveaux pour le français à l'aide de contraintes basées sur les structures de traits typées
Bibliographic record
Abstract
Cet article illustre l'application de techniques modernes en linguistique computationnelle et génie linguistique, connues sous le nom de "morphologie à deux niveaux" (MON), "linguistique basée sur les contraintes" et "structures de traits typés" (SST), pour la construction de lexiques en français. Ces techniques s'inspirent de concepts présentés dans la littérature consacrée aux grammaires syntagmatiques guidées par les têtes (HPSG) et à la MON pour le traitement de la morphologie flexionnelle du français. Le formalisme dans lequel le lexique a été réalisé est ALEP (advanced language engineering platform), conçu pour associer expressivité et efficacité. Les deux modules intervenant dans la construction du lexique sont le composant à deux niveaux et le composant morphosyntaxique. Leur description ainsi que leurs rôles respectifs sont présentés et illustrés en détail.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".