Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Bibliographic record
Abstract
On behalf of the Program Committee for NAACL 2000, I am pleased to present you with the papers accepted for presentation at the First Meeting of the North American Chapter of the Association for Computational Linguistics, held in Seattle, Washington, April 29-May 4, 2000.NAACL received a gratifyingly large number of papers from around the world. Submissions were received from 28 countries. Reviewing was blind to all reviewers and area chairs. It was also highly selective. Out of 166 submissions, 43 were selected for presentation at NAACL 2000.Selecting the papers was not an easy task. In total, over 110 reviewers, representing 20 different countries, reported to a senior program committee consisting of eight area chairs. The senior program committee spent an intensive day at a meeting in Virginia reaching the final decisions. The area chairs and reviewers cannot be thanked enough for the conscientious and painstaking jobs they performed. All those who contributed are named on the following page, but I would particularly like to express my thanks here to the area chairs: Michael Collins (AT&T Labs - Research), Nancy Green (University of North Carolina at Greensboro), Graeme Hirst (University of Toronto), Kevin Knight (USC/Information Sciences Institute), Dekang Lin (University of Manitoba), Diane Litman (AT&T Labs - Research), Philip Resnik (University of Maryland), and Andreas Stolcke (SRI International).In its first year, it was advantageous to co-locate NAACL with ANLP, an established conference. To coordinate the two conferences, submissions focusing on end-applications were invited to ANLP 2000, while submissions focusing on methodology were invited to NAACL 2000. Future NAACL conferences will encourage both types of submissions.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.122 | 0.075 |
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".