Adding Chinese to a multilingual terminological resource
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although there is a general consensus about the importance of providing access to combinatorial information in specialized dictionaries and term banks, few terminological resources actually record collocations. More importantly, since most terminological resources are concept-based, their structures are not adapted to the description of this linguistic phenomenon. This paper presents a methodology and descriptive model designed to include Chinese collocations in a multilingual resource which focuses on environment terminology. The methodology is corpusbased and the descriptive model (based on Explanatory and Combinatorial Lexicology (Mel’?uk et al., 1995)) aims to account for the lexico-semantic properties of collocations. We first comment on the characteristics of Chinese collocations that need to be taken into consideration and that can differ from collocations in other languages. Then, we describe the DiCoEnviro, a multilingual terminological resource on the environment, and the methodology devised to compile it. We then focus on collocations and explain how some parts of the methodology for their collection and lexicographical description need to be adapted to Chinese.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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