Experiência na criação de um dicionário bilíngue de parônimos para alunos de língua estrangeira
Bibliographic record
Abstract
In this paper, we would like to present a new version of a German-Russian and Russian-German paronym dictionary. This book is a pioneering work and so far unique of its kind to serve as a reference work for foreign language learners (Russian or German as L2 language), warning them of the dangers of confusing similar sounding words in their spoken language. The previous edition (PAVLOVA; SVETOZAROVA, 2012) includes many types of paronyms which are described in the paper, but this work needs to be expanded by more keywords and paronym groups. The previous version of this Dictionary will also be extended by a few more classes of paronyms. The Dictionary is primarily aimed at two target groups consisting of foreign language (L2) learners. In this case, these are Russians who learn German and Germans who learn Russian as second language. For L2 learners, the difference between words with high and low neighborhood density is larger than for native speakers. This is proven by both the “Slips of the tongue” Corpus we created and by modern psycholinguistic studies to which we refer in this paper. This is the reason why we include more paronym pairs and groups in our Dictionary than traditional reference works of this kind do. In this paper, we describe different types of paronyms, explain our methods for the selection of keywords, and clarify the structure of our Dictionary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".