Building specialized dictionaries using lexical functions
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
It is now widely acknowledged that terms enter into a variety of structures and that classic taxonomies and meronymies represent only a small part of the relationships terms share. This can be seen in recent specialized dictionaries that account for derivational relationships, co-occurrents, synonyms, antonyms, etc. It also has been underlined in several articles written by terminologists as well as linguists or computational scientists working with specialized corpora. This article will discuss the advanta ges and shortcomings of trying to account for semantic relations between terms using a specific framework, i.e. lexical functions (Mel’cuk et al. 1984-1999, 1995). It is based on a long-term project aimed at converting an existing paper dictionary (Dancette & Réthoré 2000) into a relational database. We will show that even if lexical functions have several advantages, a number of decisions must be made to accommodate the description of specialized terms.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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