Automated Methods of Phonosemantic Analysis of Poetic Text: Communicative and Pragmatic Aspect
Bibliographic record
Abstract
On the material of modern poetic texts of the poem "Ten Moons» in the original by the British writer S. Dugdale and translation, the phonosemantic analysis of the tone of the text using the automated system ParallelDots (and the others) is carried out. The aim of the study is to identify the effectiveness of automated systems of tonality analysis in general and in the translation of poetic literature, and to determine which of these systems have the greatest functionality, the highest accuracy and analyze the text on the greatest number of levels. Various methods of automated sentiment analysis are used: ParallelDots, SentiStrength, SentiWordNet, Social Media Monitoring Tool, VAAL, Zvukotsvet.ru. The analysis allows to establish the possibility of using this automated system in the work of a translator with the aim of self-testing for the compliance with the adequate transfer of the pragmatic potential of the poetic text in the fiction translation. The relevance and novelty of the study is beyond any doubt in view of the growing digitalization and the need to resort to different types of artificial intelligence to achieve quality and ergonomics in the translation process. The study has undoubted practical relevance: the results obtained allow us to identify the most successful automated systems for sentiment analysis of poetic discourse.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".