Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Notice bibliographique
Résumé
Welcome to the 30th year of SIGIR, the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. The growth in SIGIR over the past few years has been remarkable. SIGIR 2005 saw a record 368 full paper submissions; SIGIR 2006 again set a record with 399 submissions. In our planning for 2007 we anticipated between 350 and 400 papers. Instead, we were pleasantly surprised, but somewhat overwhelmed, when we received 490 submissions, an increase of nearly 23% over 2006. From these submissions we were able to accept 84 papers (17.5%). These contributions are drawn from the breadth of IR research, including indexing, efficiency, evaluation, formal models, machine learning, classification, and user studies. Many papers are devoted to specialized topics such as question answering, multi-media retrieval, Web search and spam. Along with these full papers, we were able to accept 105 posters, 18 demonstrations, 9 tutorials and 9 workshops. In addition, 9 PhD candidates were selected to participate in our doctoral consortium. Again this year, the selection of full papers was dependent on a two-tiered reviewing process. The PC Chairs and 33 Senior PC members, nominated nearly 313 primary reviewers. Each reviewer was assigned between 3 and 8 papers by the PC chairs in accordance to reviewers' stated subject expertise and each paper was allocated three reviewers. In cases where there was a wide range of scores or incomplete information, several primary reviewers helped us out with a number of additional reviews. The role of the Senior PC members was to oversee the review process, by resolving disagreements between reviewers and producing a meta-review for each paper. These meta-reviews served as the basis for discussion at the Programme Committee meeting. Each Senior PC member was responsible for 13 to 18 papers. Senior PC members were selected for their subject expertise in the different topic areas and attention was also paid to geographic representation and PC Committee experience. All the reviewing was double blind with the identity of authors being released only after the selection of papers was completed. We thank the members of the Senior PC for their hard work in helping us cope with the unexpected number of submissions. Similarly thorough processes were followed for the selection of posters, demonstrations, tutorials, and workshops, as well as for the selection of participants in the doctoral consortium. We are grateful for the efforts of the various chairs who managed the selection of these contributions: Gianni Amati, Chris Buckley, Thomas Hofmann, Liz Liddy, Josiane Mothe, Thomas Roelleke, Mark Sanderson, and ChengXiang Zhai. We thank Edwin van Huis, our keynote speaker, for agreeing to share his ideas with us. We thank the SIGIR executive committee for their willingness to answer our many questions quickly and carefully. We thank TNO, who hosted the Program Committee meeting in Delft. Finally, we thank Wessel Kraaij and Arjen P. de Vries, the conference General Chairs for their tremendous efforts to make this conference a success. In early April we were saddened to learn of the passing of Karen Sparck Jones, one of the great pioneers of information retrieval. We are pleased to host her Athena Award lecture, and we dedicate these proceedings to her memory.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».