Proceedings of the 11th International Conference on Parsing Technologies
Bibliographic record
Abstract
Welcome to the Eleventh International Conference on Parsing Technologies, IWPT’09, in the splendid \ncity of Paris. \nIWPT’09 continues the tradition of biennial conferences on parsing technology organized by SIGPARSE, \nthe Special Interest Group on Parsing of the Association for Computational Linguistics (ACL). The first \nconference, in 1989, took place in Pittsburgh and Hidden Valley, Pennsylvania. Subsequently, IWPT \nconferences were held in Cancun (Mexico) in 1991; Tilburg (Netherlands) and Durbuy (Belgium) in \n1993; Prague and Karlovy Vary (Czech Republic) in 1995; Boston/Cambridge (Massachusetts) in 1997; \nTrento (Italy) in 2000; Beijing (China) in 2001; Nancy (France) in 2003; Vancouver (Canada) in 2005; \nand Prague (Czech Republic) in 2007. \nOver the years the IWPT Workshops have become the major forum for researchers in natural language \nparsing. They have lead to the publication of four books on parsing technologies; a fifth one about to be \npublished. \nWhere the IWPT conferences from 1989 through 2003 were standalone conferences, the last two IWPTs \nwere organised as co-satellite event of large conferences: IWPT 2005 was co-loated with the HLT- \nEMNLP conference in Vancouver, and IWPT 2007 with the main ACL conference in Prague. This \nworked well from a logistic point of view, thanks to the support from ACL, but it was felt to lead to \nsomewhat less interesting events than in the past, sitting in the shadow of the larger conference and \ncompeting with other satellite events. It was therefore decided to return to the standalone format in 2009, \nwith INRIA Rocquencourt and the University of Paris 7 volunteering to take charge of the organisation. \nWe would like to thank Eric de la Clergerie, Laurence Danlos, Benoit Sagot and the support staff at \nINRIA and University of Paris 7 for their efforts to realize IWPT’09. \nIWPT’09 is fortunate to have three very distinguished invited speakers: John Carroll from the university \nof Sussex, Mark Johnson from Brown University, and Joakim Nivre from the University of Uppsala. \nI would like to thank all the programme committee members for their careful and timely work, especially \nthose that took up extra rewiewing obligations at very short notice and those who participated in \ndiscussions on diverging reviews. Special thanks go to Eric de la Clergerie, the programme chair, \nfor organising the reviewing, designing the workshop programme and producing the proceedings. The \nscientific programme includes 14 accepted full papers and 27 accepted short papers (the latter being an \nall-time high for IWPT), covering virtually all currently hot topics in parsing technology. Together with \nthe three invited talks by top experts in parsing, these papers provide a fascinating picture of the state of \nthe art in parsing natural language, that I hope you will enjoy and will find inspiring. \nHarry Bunt \nIWPT’09 General Chair
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.167 | 0.107 |
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".