USING LANGUAGE CORPORA IN BUILDING LEARNER'S COMBINATORIAL DICTIONARIES
Bibliographic record
Abstract
The article deals with the problems of using corpus data in educational lexicography. The data from traditional collocations dictionaries, such as the Oxford Collocations Dictionary for Students of English , are compared with the data extracted from the British National Corpus (BNC). The BNC is an approximately 100-million-word corpus of written , spoken British English (it is considered as a balanced corpus that contains texts from a wide range of different language genres and text domains). A corpus manager (a web-based tool for searching and re- trieving lexical, grammatical and textual data) was employed in the study. Due to this it has become possible to analyze the data, generating frequency information, concordances (i.e. lists of all of the occurrences of a particu- lar search term in a corpus, presented within the context in which they occur), keywords, collocations or carrying out statistical tests.In addition, the data from Dictionnaire des combinaisons de mots are compared with the data from the cor- pus-based electronic dictionary Antidote of the Canadian software company Druid informatique. This program com- prises multiple dictionaries placed within a unified interface. The entry for each word displays its pronunciation, inflected forms, etymology, etc. along with their respective frequency. A frequency index is provided for each word; it indicates the relative frequency of the word in the six billion-word corpus. The presence of a dictionary of collocations that provides all the most significant combinations of the entry word with other words (functioning either as leading or dependent components), grouped by their syntactic function in the sentence and frequency is the most valuable feature of this program.The novelty of the work lies in the fact that it demonstrates the educational potential of corpus data in lexicog- raphy, in particular, in the field of compiling collocation dictionaries. The specific examples show how linguistic corpora can help comprehend the semantic, stylistic and syntactic specific features of words. The paper concludes that corpus data has many advantages over traditional dictionaries; at the same time, the limitations of corpus data in syntactic and semantic analysis are noted. In conclusion, the authors outline a project for developing a corpus-based pedagogical dictionary for students of Russian.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".