The Acadian Nool module: Automatic processing of a regional oral French
Bibliographic record
Abstract
Automated analysis of oral corpora is still in its infancy. Interest is growing, but tools are still scarce. This article presents processing tools that we have developed to analyze corpora of spontaneous oral speech in Acadian French. This variety of French spoken in the Maritime Provinces of Canada has three levels of characteristics: oral, regional, and mixed language traits. The challenge was to adapt an existing processing tool, INTEXlNooJ, to find solutions to the problems presented by our corpora. We will present three different solutions developed with NooJ: (1) the configuration of dictionary entries that allows users to relate the orthographic and lexical representations of a word coming from standard French, traditional Acadian, English, or the vernacular; (2) grammars developed to process morphological characteristics of nominal and verbal inflections; and (3) a disambiguation graph for a, which is the 3SG pronoun in Acadian French as well as the 3SG.PRES of the auxiliary avoir.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".