Automatic Lemmatization of Old English Class III Strong Verbs (L-Y) with ALOEV3
Why this work is in the frame
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Bibliographic record
Abstract
This article presents ALOEV3, a lemmatizer based on Morphological Generation that allows for the type-based automatic lemmatization of Old English Class III strong verbs beginning with the letters L–Y. The lemmatizer operates on the basis of the inflectional, derivational and morpho-phonological alternation rules characteristic of this class. The generated form-types are checked against the two most reputed Old English corpora, namely the Dictionary of Old English Corpus and The York-Toronto-Helsinki Parsed Corpus of Old English Prose to validate their attestations and assign the corresponding lemma. Results show that 97 percent of the validated forms are successfully assigned a single lemma. The remaining inflectional forms (38 out of 1,256) show competition between two lemmas, which implies that despite the high level of accuracy of the lemmatizer, contextual, token-based analysis is still needed for disambiguation. However, the research shows that competition only occurs in a limited set of lemma pairs and their derivatives. Although the research focuses on but one strong verb class, it confirms that exploring the avenues of automatic lemmatization will contribute to the field of Old English lexicography by either lemmatizing attested inflectional form types or by highlighting areas for manual revision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it