The Study of Polysemy in the Framework of Cognitive Semantics in Azerbaijani Turkish
Bibliographic record
Abstract
The aim of the present study is to investigate the role of conceptual metaphor in the polysemy of the visual verb of /baxmaq/ (look) in Azerbaijani Turkish within the framework of cognitive semantics. The research method is a descriptive—analytic and the study tries to present the various meanings of the verb. To achieve this goal, the linguistic data was gathered by using Azerbaijani Turkish dictionaries, books, magazines, newspapers, and one of the authors’ linguistic intuitions. The findings indicate that for visual verb /baxmaq/ there are 19 senses with regard to its classification and abstracting away of contextual variations. They also show that the polysemy is a systematic phenomenon and factor such as metaphor and metonymy are crucially involved in the formation and description of polysemy and this makes broad meaning and eventually leads to polysemy of verb. In this way, semantic extensive network of verb is formed and on the other hand, it is claimed that metaphor has stronger role than metonymy in the formation of polysemy and usage of verb in the different context. The findings also show that polysemy is not randomly and forms based on our experiences of different concepts and the meanings of verbs are different from each other because they are understood from different ways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".