Relations sémantiques pour l'indexation automatique. Définition d'objectifs pour la détection automatique
Bibliographic record
Abstract
L'accs aux documents numriques volumineux ou complexes peut tre facilit par un index du style que l'on retrouve la fin d'un livre, prsentant schmatiquement les concepts abords dans le document et les liens que l'auteur a tabli entre eux. Il peut s'avrer un outil prcieux dans la fouille de documents. Le travail de recherche dcrit ici vise identifier les relations smantiques prsentes dans les index de livre produits manuellement pour dterminer lesquelles peuvent tre drives automatiquement. Pour ce faire, sept index ont t examins. Les observations releves permettent de distinguer deux types de relations : celles pour lesquelles l'analyse du document en main fournit suffisamment d'informations, et celles pour lesquelles des ressources terminologiques externes sont ncessaires. Des pistes pour le dveloppement d'un systme d'indexation automatique de monographies sont ainsi identifies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".