Vers une classification sémantique fine des noms dâagent en français
Bibliographic record
Abstract
This thesis proposes a fine-grained semantic classification of what is traditionally named agent nouns in French; it is thus a study of semantic derivation. Our classification was elaborated starting from 1573 agent nouns extracted from the dictionary Le Nouveau Petit Robert. The definitions of several agent nouns in Le Nouveau Petit Robert represent paraphrastic reformulations that are sufficiently close, which allows us to distribute these nouns into semantically more specific subclasses. We have identified 22 subclasses of agent nouns; these subclasses, along with the corresponding lexical units, were described by means of the formalism of lexical functions proposed by Meaning Text theory. We also elaborated definition templates for lexical units of each subclass as well as generalized government patterns (? subcategorization frames) for them. The interest of our work lies in the fact that the suggested classification allows for a more uniform and a more coherent global description of agent nouns.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".