General Issues of Homonymy in the Persian Language
Bibliographic record
Abstract
The importance of the problem under investigation is the following: it indicates important sub-levels and lexical-semantic features of homonyms in the modern Persian language; it observes the material basis of linguistic nature of these phenomena (homonyms), elevating the issue to the level of the fundamental current problems, which are under multidimensional analysis, synthesis, generalization on a scientific level nowadays. Theoretical foundations of the common problems of homonymy in modern Persian as the main aspect of a global lexical study are represented in this paper. It was revealed that the traditional principles of general differentiation of homonyms into three common classes (lexical, morphological and lexical - grammatical) in modern Persian due to its linguistic features is not sufficient. In addition, groups of situational and mixed homonyms were detected. The investigation highlights the examples of homonymy formed by grammatical means (prefixes, suffixes); homonymy of compound words, the cases which have not been traditionally is not represented in the previous lexicological works, as well as their lexical-semantic differentiation and their interaction at the level of literary and dialectal vocabulary. The article proceedings can be are useful for the linguists, the experts of lexicology and language teachers. Also it can be recommended for undergraduates, post graduate students and students of high school with in-depth research training in the field of linguistics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".