La lemmatisation et l'encodage grammatical permettent-ils de reconnaître l'auteur d'un texte ?
Bibliographic record
Abstract
Lemmatization and Morphological lagging : their Application to Authorship Attribution. - Traditional methods of attributing an anonymous text to his own author have been increased by the outcome of linguistic statistics for a few years now. By far statistics provides a more objective way of comparing texts to one another. Textual corpora however have not often be tagged ; as researchers have not been given the opportunity to point out and systematically retrieve grammatical occurrences and features of a given corpus, there has been no other choice left than to study lexical connection between texts. The method has proved successful, results yet depend perceptibly on topics and literary genres. We will therefore proceed to analyse a classical Latin corpus in which texts have been lemmatizated and grammatically tagged. We will endeavour to examine if dissimilarity measures between texts from the study of grammatical parameters give finer and discriminating results than by lexical means. If our conclusion occurs to be positive, from now on, it is worth considering the undertaking of lemmatization of medieval Latin texts.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".