Mieux enseigner la grammaire, pistes didactiques et activités pour la classe . Suzanne G. Chartrand (dir.), Montréal, Pearson ERPI, Editions du renouveau pédagogique, collection Education, 2016, 346 p., ISBN : 978-2-7613-7870-3.
Bibliographic record
Abstract
Le modèle wordnet est le plus répandu des modèles de représentation de la sémantique lexicale reposant sur un inventaire de sens a priori. A la suite du Princeton WordNet de l’anglais, des ressources de type wordnet ont été développées pour plusieurs dizaines de langues, dont le français, le plus souvent au moyen de techniques automatiques ou semi-automatiques. Dans cet article, nous revenons tout d’abord sur les caractéristiques et les limites du modèle wordnet. Nous dressons ensuite un panorama des méthodes utilisées pour le développement de wordnets, avant d’illustrer nos propres travaux dans ce domaine par le développement du WOLF, le WOrdnet Libre du Français.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".