Webaffix : une boîte à outils d’acquisition lexicale à partir du Web
Bibliographic record
Abstract
Nous présentons ici Webaffix, un outil qui permet de constituer et d’enrichir semi-automatiquement des données lexicales en utilisant le Web comme corpus. Il permet de détecter et d’analyser morphologiquement des unités lexicales nouvelles (c’est-à-dire absentes de listes de référence telles que les dictionnaires) construites par suffixation ou préfixation. Nous présentons les techniques utilisées par Webaffix, en déclinant les différents modes d’utilisation que nous avons envisagés et mis en pratique, ainsi que des exemples de résultats produits par diverses campagnes de collecte. Les données ainsi recueillies constituent des ressources lexicales pour différentes applications en traitement automatique des langues, mais également pour l’étude à grande échelle de la morphologie dérivationnelle.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".