Automatic Lemmatization of Old English Class III Strong Verbs (L-Y) with ALOEV3
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".