Why Lexical Semantics is Important for E-Lexicography and Why it is Equally Important to Hide its Formal Representations from Users of Dictionaries
Bibliographic record
Abstract
This paper reviews different ways in which lexical semantics models can be applied to e-lexicography with a focus on specialized dictionaries for learners. I show how components of these models can be implemented in the entries of digital dictionaries and how they help lexicographers systematize lexical relations between lexical units (in the same language or across different languages). This being said, users of dictionaries cannot be requested to familiarize themselves with different theoretical frameworks. I present various strategies to present the data contained in entries that exploit the potential of formal and systematic descriptions while keeping the technical concepts and metalanguage associated with theoretical frameworks in the background. More specifically, I show how these models and strategies were implemented in two multilingual online specialized dictionaries, i.e. the DiCoInfo (Dictionnaire fondamental de l’informatique et de l’Internet) and the DiCoEnviro (Dictionnaire fondamental de l’environnement).
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.013 | 0.052 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".