Exploring the Lexis of Art Through a Specialized Corpus: A Bilingual Italian-English Perspective
Bibliographic record
Abstract
This study presents an application of a specialized corpus, including texts specifically related to art and cultural heritage, to the analysis of artistic vocabulary in a bilingual (Italian-English) perspective, focusing on the Italian lemmas opera, figura and disegno and their English translation equivalents. The starting point is the Italian corpus that is being developed under the research project Lessico multilingue dei beni culturali (‘Multilingual art and cultural heritage vocabulary’, LBC), available online in open access through NoSketchEngine. First, a lemmatized nounlist ordered by frequency of occurrence is extracted from the corpus, leading to the selection of the above-mentioned focus words, in view of both their frequency and status as technical terms within the domain of art (though exhibiting different levels of technicality). These are further investigated by extracting collocates and KWIC concordances, leading to the identification of several specialized collocations and domain-specific senses. The analysis subsequently moves from corpus to dictionary, exploring the extent to which the patterns emerging from corpus investigation are accounted for in the entries for opera, figura and disegno in four Italian-English bilingual dictionaries. From this viewpoint, the study also aims to show how specialized corpus data can be used for the extraction of collocations, terms, and context-specific word senses, which may in turn be used both to enrich the information provided by currently available general dictionaries, and to work towards the creation of a large-scale specialized bilingual dictionary, which is non-existent to date.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".