Report on the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR '15)
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
The amount of structured content published on the Web has been growing rapidly, making it possible to address increasingly complex information access tasks. Recent years have witnessed the emergence of large scale human-curated knowledge bases as well as a growing array of techniques that identify or extract information automatically from unstructured and semi-structured sources. The ESAIR workshop series aims to advance the general research agenda on the problem of creating and exploiting semantic annotations. The eighth edition of ESAIR took place at CIKM 2015 in Melbourne, Australia, on the 23rd of October. Having a special focus on applications, we dedicated an "annotations in action" track to demonstrations that showcase innovative prototype systems, in addition to the regular research and position paper contributions. The workshop also featured invited talks from leaders in the field. This report presents an overview of the event and its major outcomes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it