Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Bibliographic record
Abstract
Thank you for attending AND 2010! Fourth workshop in the AND series contains highquality work spanning a large array of disciplines related to the treatment of Noisy text. This year AND is collocated with CIKM so the flavor of IR and KM shows through in the AND program as well. The final program consists of 11 papers selected from 21 submissions. Each paper was carefully reviewed by three Program Committee members. We would like to thank our Program Committee for selecting this high-quality program for AND 2010. Papers in which a student is the primary author (first author/presenter) will be eligible for the IAPR Best Student Paper Award. initial short listing for this award has been done based on the reviews. final decision will be made during the workshop based on the presentations. Also the best papers from the workshop will appear in a special issue of the International Journal on Document Analysis and Recognition after going through further reviewing. selection for this is based on the reviews of the AND PC members. We are excited to have Randy Geobel, University of Alberta, Canada, as the key note speaker. His talk is very interestingly titled The nature of noise in linguistic corpora.
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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".