Lemmatising Treebanks. Corpus Annotation with Knowledge Bases
Bibliographic record
Abstract
espanolEste articulo se centra en la lexicografia del ingles antiguo y el analisis de corpus. El objetivo es definir un procedimiento de lematizacion para un tipo de corpus del ingles antiguo anotado y parseado conocido como treebank. Este estudio se centra en dos cuestiones, concretamente en indicar donde se encuentran los datos con los que se puede lematizar el treebank del ingles antiguo; y que procedimiento debe adoptarse para enlazar la lematizacion disponible en las fuentes con el treebank. A partir de las bases de conocimiento del Proyecto Nerthus, se disena, pone en practica y evalua un procedimiento semiautomatico para dotar The York-Toronto-Helsinki Parsed Corpus of Old English Prose de etiquetas de lemas. EnglishThis article deals with Old English lexicography and corpus analysis. It aims at devising a lemmatisation procedure for a type of annotated and parsed corpus of Old English known as treebank. This study addresses two questions, namely where to find the data with which an Old English treebank can be lemmatised; and what procedure should be adopted to link the lemmatisation available from the sources to the treebank. On the grounds of the set of knowledge bases compiled by the Nerthus Project, a semi-automatic procedure for annotating The York-Toronto-Helsinki Parsed Corpus of Old English Prose with lemma tags is devised, illustrated and assessed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".