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Community Profiling for Crowdsourcing Queries

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CommunityProfiling_belhajjame_brambilla.pdf (195.9Kb)
Date
2014
Titre de la collection
COST European cooperation in science and technology
Indexation documentaire
Informatique générale
Subject
crowdsourcing; Keyword search; Keyword search in Relational databases; Metadata search
URI
https://basepub.dauphine.fr/handle/123456789/16699
Collections
  • LAMSADE : Publications
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Auteur
Belhajjame, Khalid
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Brambilla, Marco
20606 Dipartimento di Elettronica e Informazione
Grigori, Daniela
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Mauri, Andrea
20606 Dipartimento di Elettronica e Informazione
Type
Rapport
Résumé en anglais
Crowdsourcing has recently gained momentum as a means for outsourcing tasks to human workers with no specific expertise. This is a viable option for tasks that are difficult to achieve correctly using machines or that are too expensive to be carried out by expert professionals. This leads to the possibility of addressing large campaigns of jobs without facing extremely high cost. While this technique has proven to be effective for simple labeling tasks, such as text recognition, the quality of results it delivers for relatively sophisticated tasks such as scientific paper translation, may appear as deteriorated. The objective of our work is instead to define in a rich way the concept of community of workers, defining a way to properly match crowdsourcing tasks to the communities based on expertise of workers and field/topic of tasks. We describe a set of conceptual models for queries, communities and workers, a high level architecture of the approach, and outline a set of possible strategies for addressing various aspects of expert-targeted crowdsourcing.

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