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Example queries for CLEF e-Health 2015
Task 2: User-Centred Health Information Retrieval
Last update: 5 Mar 2015
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Files:
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* clef2015.training.queries-EN.txt
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This file contains 5 example queries (in English).
This year we will explore circumlocutory queries that users may pose when
faced with signs and symptoms of a medical condition.
To form the query set, we adopted the method recently
investigated by [1,2].
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* AR/
clef2015.training.queries-AR.txt
clef2015.training.queries-AR-auto.txt
* CS/
clef2015.training.queries-CS.txt
clef2015.training.queries-CS-auto.txt
* DE/
clef2015.training.queries-DE.txt
clef2015.training.queries-DE-auto.txt
* ES/
clef2015.training.queries-ES.txt
clef2015.training.queries-ES-auto.txt
* FA/
clef2015.training.queries-FA.txt
clef2015.training.queries-FA-auto.txt
* FR/
clef2015.training.queries-FR.txt
clef2015.training.queries-FR-auto.txt
* IT/
clef2015.training.queries-IT.txt
clef2015.training.queries-IT-auto.txt
* PT/
clef2015.training.queries-PT.txt
clef2015.training.queries-PT-auto.txt
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Every query was translated from English to a target language (AR: Arabic,
CS: Czech, DE: German, ES: Spanish, FA: Farsi, FR: French, IT: Italian,
PT: Portuguese).
We also provide a baseline translation made by Google Translator (files containing
"auto" in their names).
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* translations.xml
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We provide the translations.xml file for an easier visualization
of the automatically generated translations.
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* clef2015.qrels.training.txt
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A pool containing the top 10 results of different systems was made and
manually evaluated. Only the query field of each topic was used.
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* clef2015.qrels.training.additional.txt
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The same procedure described above was used to generate this file, but
the disease name from <narr> field was manually appended to the query.
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References:
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[1] Isabelle Stanton, Samuel Ieong, Nina Mishra. SigIR 2014.
[2] Guido Zuccon, Bevan Koopman and Joao Palotti. ECIR 2015.
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