A Corpus-Based Approach to the Lemmatisation of Old English Superlative Adverbs
Bibliographic record
Abstract
The aim of this article is to discuss the lemmatisation process of Old English adverbs inflected for the superlative from a corpus-based perspective. This study has been conducted on the basis of a semi-automatic methodology through which the inflectional forms have been automatically extracted from The York-Toronto-Helsinki Parsed Corpus of Old English Prose and The York Toronto- Helsinki Parsed Corpus of Old English Poetry whereas the task of assigning a lemma has been completed manually. The list of adverbial lemmas amounts to 1,755 and has been provided by the lexical database of Old English Nerthus. Additionally, the resulting lemmatised list has been checked against the lemmatised forms compiled by the Dictionary of Old English and Seelig’s (1930) work on Old English comparative and superlative adjectives and adverbs. Through this comparison it has been possible to verify doubtful forms and incorporate new ones that are unattested by the YCOE. This pilot study has implemented for the first time a methodology for the lemmatisation of a non-verbal class and can be further applied to those categories that are still unlemmatised, namely nouns and adjectives.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".