Difference between revisions of "Sinai at Imageclef Wikipedia Retrieval Task 2011: Testing Combined Systems"

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{{Infobox work
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| title = Sinai at Imageclef Wikipedia Retrieval Task 2011: Testing Combined Systems
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| date = 2011
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| authors = [[Miguel Ángel García Cumbreras]]<br />[[Manuel Carlos Díaz-Galiano]]<br />[[Luis Alfonso Ureña López]]<br />[[Javier Arias Buendía]]
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| link = http://ceur-ws.org/Vol-1177/CLEF2011wn-ImageCLEF-Garcia-CumbrerasEt2011.pdf
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}}
 
'''Sinai at Imageclef Wikipedia Retrieval Task 2011: Testing Combined Systems''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Miguel Ángel García Cumbreras]], [[Manuel Carlos Díaz-Galiano]], [[Luis Alfonso Ureña López]] and [[Javier Arias Buendía]].
 
'''Sinai at Imageclef Wikipedia Retrieval Task 2011: Testing Combined Systems''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Miguel Ángel García Cumbreras]], [[Manuel Carlos Díaz-Galiano]], [[Luis Alfonso Ureña López]] and [[Javier Arias Buendía]].
  
 
== Overview ==
 
== Overview ==
 
Several researches demonstrate that the use and integration of various knowledge sources improves the quality and efficiency of information systems. This paper presents the system developed by the SINAI research group at the ImageCLEF [[Wikipedia]] Retrieval task. Using only the English text associated with each image or its translation from French or German, system applies several combinations of the textual tags and the combination of [[information retrieval]] (IR) systems. The influence of [[machine translation]], the use of the different annotation tags and the retrieval results from different IRs systems are the main aims of this work. The obtained results show that the election of the annotation tags and the IR system have influence in the results, and some fusions of the retrieved lists can improve the performance.
 
Several researches demonstrate that the use and integration of various knowledge sources improves the quality and efficiency of information systems. This paper presents the system developed by the SINAI research group at the ImageCLEF [[Wikipedia]] Retrieval task. Using only the English text associated with each image or its translation from French or German, system applies several combinations of the textual tags and the combination of [[information retrieval]] (IR) systems. The influence of [[machine translation]], the use of the different annotation tags and the retrieval results from different IRs systems are the main aims of this work. The obtained results show that the election of the annotation tags and the IR system have influence in the results, and some fusions of the retrieved lists can improve the performance.

Revision as of 08:19, 17 May 2020


Sinai at Imageclef Wikipedia Retrieval Task 2011: Testing Combined Systems
Authors
Miguel Ángel García Cumbreras
Manuel Carlos Díaz-Galiano
Luis Alfonso Ureña López
Javier Arias Buendía
Publication date
2011
Links
Original

Sinai at Imageclef Wikipedia Retrieval Task 2011: Testing Combined Systems - scientific work related to Wikipedia quality published in 2011, written by Miguel Ángel García Cumbreras, Manuel Carlos Díaz-Galiano, Luis Alfonso Ureña López and Javier Arias Buendía.

Overview

Several researches demonstrate that the use and integration of various knowledge sources improves the quality and efficiency of information systems. This paper presents the system developed by the SINAI research group at the ImageCLEF Wikipedia Retrieval task. Using only the English text associated with each image or its translation from French or German, system applies several combinations of the textual tags and the combination of information retrieval (IR) systems. The influence of machine translation, the use of the different annotation tags and the retrieval results from different IRs systems are the main aims of this work. The obtained results show that the election of the annotation tags and the IR system have influence in the results, and some fusions of the retrieved lists can improve the performance.