Raisonnement à base de cas textuels Etat de l'art et perspectives
Bibliographic record
Abstract
RESUME. Traditionnelle ment le raisonnement a base de cas (CBR) s’appuie sur des experiences decrites dans des formats completement structures tels que des objets ou des enregistrements de base de donnees. Toutefois d’autres modeles ont ete proposes pour surmonter les limitations de cette approche structurelle et rendre possible l’application a des domaines plus varies. Dans cet article, nous passons en revue les extensions du formalisme CBR proposees pour traiter des experiences decrites dans des documents textuels, travaux regroupes sous la banniere CBR textuel. Apres une presentation succincte des principes generaux du raisonnement a base de cas, nous decrivons les principaux travaux du CBR textuel et nous les comparons selon differents aspects techniques et applicatifs. Finalement, nous proposons quelques problemes et avenues de recherche meritant d’etre explores dans des travaux futurs.
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 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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".