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Overview of the CLEF eHealth Evaluation Lab 2021
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In: Experimental IR Meets Multilinguality, Multimodality, and Interaction ; https://hal.archives-ouvertes.fr/hal-03369846 ; Experimental IR Meets Multilinguality, Multimodality, and Interaction, 12880, Springer International Publishing, pp.308-323, 2021, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-85251-1_21⟩ (2021)
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Overview of the CLEF 2019 Personalised Information Retrieval Lab (PIR-CLEF 2019)
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In: CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction ; https://hal.archives-ouvertes.fr/hal-03156689 ; CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction, pp.417-424, 2019, ⟨10.1007/978-3-030-28577-7_31⟩ (2019)
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Overview of the CLEF eHealth Evaluation Lab 2019
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In: CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction pp 322-339 ; https://hal.archives-ouvertes.fr/hal-03156710 ; CLEF 2019: Experimental IR Meets Multilinguality, Multimodality, and Interaction pp 322-339, pp.322-339, 2019, ⟨10.1007/978-3-030-28577-7_26⟩ (2019)
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Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes
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The Scholarly Influence of the CLEF eHealth Initiative by the Conference and Labs of the Evaluation Forum: Review and Bibliometric Study of the 2012-2017 Outcomes.
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Experimental IR meets multilinguality, multimodality, and interaction: 8th international conference of the CLEF association, CLEF 2017, Dublin, Ireland, September 11-14, 2017, proceedings
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CLEF 2017 NewsREEL Overview: A Stream-based Recommender Task for Evaluation and Education
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CLEF 2017 dynamic search evaluation lab overview
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Abstract:
In this paper we provide an overview of the first edition of the CLEF Dynamic Search Lab. The CLEF Dynamic Search lab ran in the form of a workshop with the goal of approaching one key question: how can we evaluate dynamic search algorithms? Unlike static search algorithms, which essentially consider user request’s independently, and which do not adapt the ranking w.r.t the user’s sequence of interactions, dynamic search algorithms try to infer from the user’s intentions from their interactions and then adapt the ranking accordingly. Personalized session search, contextual search, and dialog systems often adopt such algorithms. This lab provides an opportunity for researchers to discuss the challenges faced when trying to measure and evaluate the performance of dynamic search algorithms, given the context of available corpora, simulations methods, and current evaluation metrics. To seed the discussion, a pilot task was run with the goal of producing search agents that could simulate the process of a user, interacting with a search system over the course of a search session. Herein, we describe the overall objectives of the CLEF 2017 Dynamic Search Lab, the resources created for the pilot task and the evaluation methodology adopted.
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Keyword:
Electronic computers. Computer science
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URL: https://strathprints.strath.ac.uk/63353/
https://strathprints.strath.ac.uk/63353/1/Kanoulas_Azzopardi_LNCS_2017_CLEF_2017_dynamic_search_evaluation_lab.pdf
https://doi.org/10.1007/978-3-319-65813-1_31
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