Abstract
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.