Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
In 2005, the Human Language Technology Conference (HLT) and the Conference on Empirical Methods in Natural Language Processing (EMNLP) were held together as a joint conference for the first time. The conference was co-sponsored by the organization traditionally behind HLT, the Human Language Technology Advisory Board, and the organization traditionally behind EMNLP, SIGDAT: The Association for Computational Linguistics (ACL) Special Interest Group on linguistic data and corpus-based approaches to natural-language processing. The joint conference was held in Vancouver, B.C., Canada on October 6--8, co-located with the 2005 Document Understanding Conference (DUC) and the 9th International Workshop on Parsing Technologies (IWPT).In the HLT tradition, the conference especially encouraged submissions involving synergistic combinations of language technologies from the sometimes disjoint areas of natural-language processing, speech processing, and information retrieval. To encourage such cross-fertilization, each of the major chair positions were filled by three people, one from each of these research areas.
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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