Identifying equivalents of specialized verbs in a bilingual comparable corpus of judgments: A frame-based methodology
Bibliographic record
Abstract
Multilingual terminological resources do not always include the equivalents of specialized verbs that occur in legal texts.This study aims to bridge that gap by proposing a methodology to assign the equivalents of this kind of predicative units.We use a comparable corpus of judgments produced by the Supreme Court of Canada and by the Supremo Tribunal de Justiça de Portugal.From this corpus, 200 English and Portuguese verbs are selected.The description of the verbs is based on the theory of Frame Semantics (Fillmore 1977(Fillmore , 1977(Fillmore , 1982(Fillmore , 1985) ) as well as on the FrameNet methodology (Ruppenhofer et al. 2010).Specialized verbs are said to evoke a semantic frame, a sort of conceptual scenario in which a number of mandatory elements play specific roles (e.g. the role of judge, the role of defendant).Given that semantic frames are language independent to a fair degree (Boas 2005; Baker 2009), the labels attributed to each of the 76 identified frames (e.g.[Crime], [Regulations]) were used to group together 165 pairs of candidate equivalents.71% of them are full equivalents, whereas 29% are only partial equivalents.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".