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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".