Definition patterns for predicative terms in specialized lexical resources
Bibliographic record
Abstract
The research presented in this paper is part of a larger project on the semi-automatic generation of definitions of semantically-related terms in specialized resources. The work reported here involves the formulation of instructions to generate the definitions of sets of morphologically-related predicative terms, based on the definition of one of the members of the set. In many cases, it is assumed that the definition of a predicative term can be inferred by combining the definition of a related lexical unit with the information provided by the semantic relation (i.e. lexical function) that links them. In other words, terminographers only need to know the definition of pollute and the semantic relation that links it to other morphologically-related terms (polluter, polluting, pollutant, etc.) in order to create the definitions of the set. The results show that rules can be used to generate a preliminary set of definitions (based on specific lexical functions). They also show that more complex rules would need to be devised for other morphological pairs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".