Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Bibliographic record
Abstract
The 2009 Conference on Computational Natural Language Learning is the thirteenth in the series of annual meetings organized by SIGNLL, the ACL special interest group on natural language learning. CoNLL-2009 will be held in Boulder, CO, 4--5 June 2009, in conjunction with NAACL HLT. For our special focus this year in the main session of CoNLL, we invited papers on unsupervised, minimally supervised and semi-supervised methods in natural language learning, as well as on incremental learning methods. As with earlier CoNLLs, we encouraged papers that addressed these issues from the perspective both of human language acquisition and of NLP systems. We received 70 submissions to the main session on these and other relevant topics, of which 11 were withdrawn. Of the remaining 59 papers, 15 were selected to appear in the conference program as oral presentations, and 10 were chosen as posters. All accepted papers appear here in the proceedings.
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 | 0.000 |
| 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 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".