Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Bibliographic record
Abstract
The 2010 Conference on Computational Natural Language Learning is the fourteenth in the series of annual meetings organized by SIGNLL, the ACL special interest group on natural language learning. CONLL-2010 will be held in Uppsala, Sweden, 15-16 July 2010, in conjunction with ACL. For our special focus this year in the main session of CoNLL, we invited papers relating to grammar induction, from a machine learning, natural language engineering and cognitive perspective. We received 99 submissions on these and other relevant topics, of which 18 were eventually withdrawn. Of the remaining 81 papers, 12 were selected to appear in the conference programme as oral presentations, and 13 were chosen as posters. All accepted papers appear here in the proceedings. Following the ACL 2010 policy we allowed an extra page in the camera ready paper for authors to incorporate reviewer comments, so each accepted paper was allowed to have nine pages plus any number of pages containing only bibliographic references. As in previous years, CoNLL-2010 has a shared task, Learning to detect hedges and their scope in natural language text. The Shared Task papers are collected into an accompanying volume of CoNLL-2010.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.089 | 0.032 |
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".