Bibliographic record
Abstract
Understanding the semantic relations between terms in specialized texts is of critical importance in translation and terminology, and generally speaking in learning from texts. Our research highlights the advantages of formalizing them in order to build hierarchies and sets of horizontal conceptual relations (i.e. process-oriented relations) for knowledge acquisition. This paper discusses a method for extracting domain-specific semantic relations in specialized texts. Obviously, some texts are more appropriate than others in this regard. ‘Knowledge-rich’ texts such as encyclopaedia and textbooks are considered good materials because of the density and richness of thematic information. Considering them as such, we used the encyclopaedic articles of the Dictionnaire analytique de la distribution/Analytical Dictionary of Retailing. We retrieved over 3000 terms semantically related to all 350 headwords of the Dictionary, and grouped them into 28 classes of relations (paradigmatic, i.e. generic, specific, agent, goal, instrument, recipient, location, etc., and also syntagmatic, such as related verbs and adjectives). This paper discusses in particular the generic, agent and property relations and examines the linguistic markers that permit their retrieval.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".