Wikipedia-based Semantic Approach for Tweet Contextualization

Meriem Amina Zingla (1, 2) , Chiraz Latiri (3) , Yahya Slimani (4) , Catherine Berrut (5)

1 UCAR - Université de Carthage (Tunisie)
2 FST - Département d'informatique
3 Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique - LIPAH (Tunis, Tunisie)
4 LSI - Laboratoire des Systèmes intelligents
5 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]

The tweet contextualization task aims at providing an automatic readable summary explaining a given tweet. As tweets are very short documents, bound to 140 characters, and not always written maintaining proper spellings, there is indeed a need for such a task. This article describes a semantic tweet expansion approach for the tweet con-textualization task based on Wikipedia as an external knowledge source. This approach consists in two major phases, namely: the first is the generation of the candidate terms, from Wikipedia. While the second is the selection of the most-related terms. To achieve this latter, we propose a semantic relatedness measure based on the Explicit Semantic Analysis and association rules mining. The e↵ectiveness of our approach is proved through an experimental study conducted on the INEX 2014 collection. Our results have outperformed of the runs issued from INEX 2014.

Mots clés: Information Retrieval; Tweet Contextualization; Query Expansion; Explicit Semantic Analysis; Association Rules Mining

Domaines: Recherche d'information [cs.IR]

Meriem Amina Zingla, Chiraz Latiri, Yahya Slimani, Catherine Berrut. Wikipedia-based Semantic Approach for Tweet Contextualization. KDDA - Knowledge Discovery ans Data Analysis, Nov 2015, Alger, Algeria. ⟨hal-01576443⟩