The Polymorphic Behaviour of Adjectives in Terminography
Bibliographic record
Abstract
Adjectives, while analyzed quite thoroughly in the works of terminologists, have not received enough attention in terminographic applications. Their inclusion, for instance in terminological databases, is low in comparison to that of nouns. However, the study of a specialized language such as karstology shows that adjectives have significant value due to their polymorphic behaviour. Our study shows that, depending on their semantic and syntactic relations with other lexical units, adjectives can provide us with essential information for terminology management and specialized knowledge organization. Semantically, adjectives highlight attributes of object concepts within a field allowing translators to discover more specific concepts, or different aspects of already known concepts. Syntactically, adjectives can present various syntagmatic relations with nouns. While some adjectives constrain the meaning of the noun, constructing other specific lexical units, others reveal the usage of terms (either complex or simple) in context. Our study, which is based on an analysis of a monolingual English corpus, has as its purpose to identify the most relevant attributive adjectives and explain their role in a specialized language such as karstology. We will also reflect on the presence of these adjectives in terminological dictionaries and on their contribution to the conceptual structure of a knowledge field.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".