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Information Retrieval Journal

, Volume 21, Issue 4, pp 337–367 | Cite as

Hybrid query expansion model for text and microblog information retrieval

  • Meriem Amina ZinglaEmail author
  • Chiraz Latiri
  • Philippe Mulhem
  • Catherine Berrut
  • Yahya Slimani
Article
  • 371 Downloads

Abstract

Query expansion (QE) is an important process in information retrieval applications that improves the user query and helps in retrieving relevant results. In this paper, we introduce a hybrid query expansion model (HQE) that investigates how external resources can be combined to association rules mining and used to enhance expansion terms generation and selection. The HQE model can be processed in different configurations, starting from methods based on association rules and combining it with external knowledge. The HQE model handles the two main phases of a QE process, namely: the candidate terms generation phase and the selection phase. We propose for the first phase, statistical, semantic and conceptual methods to generate new related terms for a given query. For the second phase, we introduce a similarity measure, ESAC, based on the Explicit Semantic Analysis that computes the relatedness between a query and the set of candidate terms. The performance of the proposed HQE model is evaluated within two experimental validations. The first one addresses the tweet search task proposed by TREC Microblog Track 2011 and an ad-hoc IR task related to the hard topics of the TREC Robust 2004. The second experimental validation concerns the tweet contextualization task organized by INEX 2014. Global results highlighted the effectiveness of our HQE model and of association rules mining for QE combined with external resources.

Keywords

Information retrieval Query expansion Tweets search Explicit Semantic Analysis Tweet contextualization wikipedia dbpedia Association rules Ad-hoc IR task 

Notes

Acknowledgements

This work is partially supported by the French-Tunisian project PHC-Utique RIMS-FD 14G 1404.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Meriem Amina Zingla
    • 1
    Email author
  • Chiraz Latiri
    • 2
  • Philippe Mulhem
    • 3
  • Catherine Berrut
    • 3
  • Yahya Slimani
    • 2
  1. 1.Faculty of Sciences of TunisTunis EL Manar UniversityTunisTunisia
  2. 2.Higher Institute of Multimedia Arts of ManoubaManouba UniversityManoubaTunisia
  3. 3.MRIM Group, LIG laboratoryGrenoble Alpes UniversityGrenobleFrance

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