A Corpus-Based Approach to Lexicography: A New English-Russian Phraseological Dictionary
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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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".