Procédures de désambiguïsation pour les systèmes de recherche d’information
Bibliographic record
Abstract
Nous discutons de la nécessité de tenir compte de la polysémie nominale pour les systèmes de recherche d’information qui tiennent compte du contenu des textes numérisés. Nous présentons un prototype qui fonctionne en identifiant les substantifs d’un texte donné et en stipulant les domaines qui leur sont rattachés afin de faire ressortir une dominante, et ainsi de procéder au typage du texte en termes de domaine. Ce prototype a pour principale particularité d’utiliser le système intex et de faire appel aux descriptions formalisées du français effectuées au Laboratoire de Linguistique Informatique implémentées sous forme de dictionnaires électroniques et de grammaires locales. Nous montrons comment intex, en s’appuyant sur ces dictionnaires et ces grammaires, peut lever des ambiguïtés relatives à des substantifs.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".