Using Ancillary Text to Index Web-based Multimedia Objects
Bibliographic record
Abstract
PériCulture is the name of a research project at the Université de Montréal which is part of a larger project based at the Université de Sherbrooke. The parent project aimed to form a research network for managing Canadian digital cultural content. The general research objective of PériCulture was to study indexing methods for web-based non-textual cultural content, specifically still images. The research results reported here build on work in image indexing and automatic (text) indexing by studying properties of text associated with images in a networked environment to try to gain some understanding of how the ancillary text associated with images on web pages can be exploited to index the corresponding images. We studied this question in the context of selected web sites, i.e. that contained multimedia objects, that had text associated with these objects (broader than file names and captions), that were bilingual (English and French), and that housed Canadian digital cultural content. We identified keywords that were useful in indexing and studied their proximity to the object described. Potential indexing terms were identified in various HTML tags and full text (each considered a different source of ancillary text). Our study found that a large number of useful indexing terms are available in the ancillary text of many web sites with cultural content, and that ancillary text of different sources have variable usefulness in retrieval. Our results suggest that these terms can be manipulated in a number of ways in automated retrieval systems to improve search results.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".