Vers une grammaire d'unification Sens-Texte du français : le temps verbal dans l'interface sémantique-syntaxe
Bibliographic record
Abstract
This thesis has two main objectives. First, we aim at describing French verbal inflection, more particularly tense in the semantics-syntax interface. Second, we want to develop the Meaning-Text unification grammar (MTUG) formalism and test its adequacy for language description. To achieve our first goal, we propose a lexicography-inspired methodology that is centered on the linguistic sign as a whole. At the descriptive level, our main thesis is that French has not only one, but two inflectional categories of tense that complement each other. The category of shifting comprises the grammemes SHIFTED and NON-SHIFTED, which locate a temporal reference point in relation with the time of speech. The category of tense proper comprises the grammemes ANTERIOR, SIMULTANEOUS and POSTERIOR, which situate facts in relation with this reference point. At the formal level, our main contributions to the MTUG formalism are the representation of structures in terms of objects and functions on these objects, as well as the modeling of semantic decompositions. The formalization of our French inflection model turns out to be an interesting way of testing MTUG's adequacy, since this phenomon involves signs of different natures. It also provides a good opportunity to observe the articulation of the grammar's modules.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".