INEX2014: Tweet Contextualization Using Association Rules between Terms

نویسندگان

  • Meriem Amina Zingla
  • Mohamed Ettaleb
  • Cherif Chiraz Latiri
  • Yahya Slimani
چکیده

Tweets are short messages that do not exceed 140 characters. Since they must be written respecting this limitation, a particular vocabulary is used. To make them understandable to a reader, it is therefore necessary to know their context. In this paper, we describe our approach submitted for the tweet contextualization track in CLEF 2014 (Conference and Labs of Evaluation Forums). This approach allows the extension of the tweet’s vocabulary by a set of thematically related words using mining association rules between terms.

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تاریخ انتشار 2014