Lost in Collocation: When Arabic Collocation Dictionaries Lack Collocations
Bibliographic record
Abstract
Arabic has always been in bad need of collocation dictionaries. However, the only two recent English/Arabic & Arabic/English dictionaries that emerged, Dar El-Ilm’s Dictionary of Collocations (DEDC) and Al-Hafiz Arabic Collocations Dictionary (AACD) suffer from serious problems. Although DEDC has a wide range of items covered, it suffers from the serious problem of disregarding the Arabic legacy of collocational equivalents while translating the English terms. English collocation structures, therefore, are translated into free Arabic word combinations. AACD, on the other hand, has the perceived problem of the deficiency in the range of items covered for each entry, ignoring that Arabic, a lexically rich language, can provide a remarkable range of collocational material on different word entries. The two dictionaries would be of greater help for language learners and translation practitioners if those problems were addressed. This paper focuses on those particular weaknesses putting forward alternative suggestions about how to tackle the deficiencies.
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 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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".