Tweet Contextualization Using Association Rules Mining and DBpedia

  • Meriem Amina Zingla
  • Chiraz Latiri
  • Yahya Slimani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9283)


Tweets are short 140 characters-limited messages that do not always conform to proper spelling rules. This spelling variation makes them hard to understand without some kind of context. For these reasons, the tweet contextualization task was introduced, aiming to provide automatic contexts to explain the tweets. We present, in this paper, two tweet contextualization approaches. The first is an inter-term association rules mining-based method, the second one, however, makes use of the DBpedia ontology. These approaches allow us to augment the vocubulary of a given tweet with a set of thematically related words. We conducted an experimental study on the INEX2014 collection to prove the effectiveness of our approaches, the obtained results are very promising.


Information retrieval Tweet contextualization track Query expansion DBpedia Association rules 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Meriem Amina Zingla
    • 1
    • 2
  • Chiraz Latiri
    • 3
  • Yahya Slimani
    • 1
  1. 1.INSAT, LISI Research LaboratoryUniversity of CarthageTunisTunisia
  2. 2.Faculty of Sciences of TunisUniversity of Tunis El ManarTunisTunisia
  3. 3.Faculty of Sciences of Tunis, LIPAH Research LaboratoryUniversity of Tunis El ManarTunisTunisia

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