A Corpus-Based Approach to Lexicography: A New English-Russian Phraseological Dictionary
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the principles of constructing the first English-Russian phraseological dictionary based on corpus data. The purpose of the present research is to introduce a methodology for organizing the selected items in a corpus-searchable phraseme list of a dictionary, to discuss linguistic issues presenting difficulties for bilingual lexicography and to analyze semantic asymmetry between English and Russian phrasemes. To achieve this goal, the following methodology has been introduced: analyzing and retrieving idioms from monolingual and bilingual idiomatic dictionaries, determining the degree of frequency of the selected idioms, considering variants of idioms and arranging them in a systematic way, and developing an idiom list. A phraseme is used in this article as a general term for a multi-word phrase with at least one fixed component. The article demonstrates the advantages of compiling a phraseological bilingual dictionary based on an analysis of corpus data and using authentic examples in the lexicographic description of phrasemes. Using corpora provides a new perspective on the contextual behavior of phrasemes and restrictions of their usage. The paper discusses the impact of using parallel English and Russian corpora for analysis of non-trivial features of English phrasemes, in comparison with their Russian equivalents, in the process of constructing an English-Russian phraseological dictionary. After an introduction, the article presents the methodology and data applied in the research and then discusses the results of the study; the author provides evidence of the advantages of using corpora in bilingual lexicography.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it