The Lemmatisation of Old English Comparative Adverbs
Bibliographic record
Abstract
Este artículo presenta un estudio piloto sobre la lematización de los adverbios comparativos del inglés antiguo. Esta investigación contribuye a la metodología previamente implementada en la lematización de las clases verbales. Los corpus The York-Toronto-Helsinki Parsed Corpus of Old English Prose y The York-Toronto-Helsinki Parsed Corpus of Old English Poetry han proveido las formas flexivas a lematizar. El punto de partida de este estudio es la extracción automática de las formas morfológicamente etiquetadas con la etiqueta ADVR (adverbios comparativos). En segundo lugar, se ha asignado un lema de la base léxica Nerthus a cada forma flexiva. En tercer lugar, los resultados han sido contrastados con Seelig (1930) y el Dictionary of Old English para verificar la asignación de lemas y desambiguar casos dudosos. Las conclusiones insisten en la aplicabilidad de este método de lematización al resto de categorías no verbales de inglés antiguo.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".