Notice bibliographique
Résumé
The TREC Web Track explores and evaluates Web retrieval technologies. The TREC 2009 Web Track included both a traditional adhoc retrieval task and a new diversity task. The goal of this diversity task is to return a ranked list of pages that together provide complete coverage for a query, while avoiding excessive redundancy in the result list. Both tasks will continue at TREC 2010, which will also include a new Web spam task. The track uses the ClueWeb09 dataset as its document collection. This collection consists of roughly 1 billion web pages in multiple languages, comprising approximately 25TB of uncompressed data crawled from the general Web during January and February 2009. For TREC 2009, topics for the track were created from the logs of a commercial search engine, with the aid of tools developed at Microsoft Research. Given a target query, these tools extracted and analyzed groups of related queries, using co-clicks and other information, to identify clusters of queries that highlight different aspects and interpretations of the target query. These clusters were employed by NIST for topic development. For use by the diversity task, each resulting topic is structured as a representative set of subtopics, each related to a different user need. Documents were judged with respect to the subtopics, as well as with respect to the topic as a whole. In 2009, a total of 18 groups submitted runs to the diversity task. To evaluate these runs, the task used two primary effectiveness measures: -nDCG as defined by Clarke et al. (SIGIR 2008) and an “intent aware” version of precision, based on the work of Agrawal et al. (WSDM 2009). Developing and validating metrics for diversity tasks continues to be a goal of the track. For TREC 2010, we will report a number of additional evaluation measures that have been proposed over the past year, including an intent aware version of the ERR measure described by Chapelle et al. (CIKM 2009). Nick Craswell from Microsoft serves as the track co-coordinator. Ian Soboroff is the NIST contact. The ClueWeb09 collection was created through the efforts of Jamie Callan and Mark Hoy at the Language Technologies Institute, Carnegie Mellon University. More information may be found on the track Web page: http://plg.uwaterloo.ca/~trecweb/2010.html. Bio Charles Clarke is a professor in the David R. Cheriton School of Computer Science at the University of Waterloo, Canada. He has published on a wide range of topics within the area of information retrieval, including papers related to evaluation, efficiency, ranking, parallel systems, security, question answering, document structure, and XML. He was a Program Co-Chair of SIGIR 2007 and General CoChair of SIGIR 2003. From 2004 to 2006 he was the coordinator of the TREC Terabyte Retrieval track. Since 2009 he has been a cocoordinator of the TREC Web Track. He is a co-author of the book Information Retrieval: Implementing and Evaluating Search Engines (MIT Press, 2010). He has previously held software development positions at a number of computer consulting and engineering firms. In 2006 he spent a sabbatical at Microsoft, where he was involved in their search engine development effort.
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 | 0,000 | 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 | 0,000 |
| Science ouverte | 0,000 | 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 ».