Genex+, a Semantic-based Automatic Extractor of Examples Applied to Bilingual Terms
Bibliographic record
Abstract
In this paper we present Genex+ (Genex plus), an improved implementation of the Genex system (Générateur d'Exemples) [17], by using the semantic closeness between certain fragments of texts.This process was based on the combination of the successive extraction of concordances and collocations with the determination of the semantic closeness by the Mutual Information method [24] and Cosine calculus [3,22].Results have proven that a good example is almost always associated to the definition of the word to which it is making reference, and that it can be extracted automatically by the consecutive restriction of semantic fields applied to fragments of general language corpora.This has the advantage of presenting a conceptual reformulation of what is said in the definition by using highly informative textual fragments, but with a less formal register.For this second version, we implemented changes required for a bilingual entry.
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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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".