Study on the influence of vocabularies used for image indexing in a multilingual retrieval environment
Bibliographic record
Abstract
Abstract The Internet constitutes a vast universe of knowledge and human culture, allowing the dissemination of ideas and information without borders. The Web also became an important media for the diffusion of multilingual resources. Linguistic differences still form a major obstacle to scientific, cultural, and educational exchange. With the ever increasing size of the Web and the availability of more and more documents in various languages, this problem becomes all the more pervasive. Besides this linguistic diversity, a multitude of databases and collections now contain documents in various formats, which may also adversely affect the retrieval process. This paper describes a research project aiming to verify the existing relations between two indexing approaches: (1) traditional image indexing recommending the use of controlled vocabularies or (2) free image indexing using uncontrolled vocabulary, and their respective performance for image retrieval, in a multilingual context. This research compares image retrieval within two contexts: a monolingual context where the language of the query is the same as the indexing language; and a multilingual context where the language of the query is different from the indexing language. This research will indicate if one of these indexing approaches surpasses the other, in terms of effectiveness, efficiency, and satisfaction of the image searchers.
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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 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".