Advances in the automatic lemmatization of Old English: class V strong verbs (L-Y)
Bibliographic record
Abstract
The grammatical description of Old English lacks complete and systematic lemmatization, which hinders Natural Language Processing studies in this language, as they strongly rely on the existence of large, annotated corpora. Moreover, the inflectional features of Old English preclude token-based automatic lemmatization. Therefore, specifically goal-oriented applications must be developed to account for the automatic lemmatization of specific variable categories. This article designs an automatic lemmatizer within the framework of Morphological Generation to address the type-based lemmatization of Old English class V strong verbs (L-Y). The lemmatizer is implemented with rules that account for inflectional, derivational and morphophonological variation. The generated forms are compared with the most relevant corpora of Old English for validation before being assigned a lemma. The lemmatizer is successful in supplying form-lemma associations not yet accounted for in the literature, and in identifying mismatches and areas for manual revision.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".