Tweet Contextualization Based on Wikipedia and Dbpedia

Bound to 140 characters, tweets are short and not written maintaining formal grammar and proper spelling. These spelling variations increase the likelihood of vocabulary mismatch and make them difficult to understand without context. This paper falls under the tweet contextualization task that aims at providing, automatically, a summary that explains a given tweet, allowing a reader to understand it. We propose different tweet expansion approaches based on Wikipeda and Dbpedia as external knowledge sources. These proposed approaches are divided into two steps. The first step consists in generating the candidate terms for a given tweet, while the second one consists in ranking and selecting these candidate terms using a similarity measure. The effectiveness of our methods is proved through an experimental study conducted on the INEX 2014 collection.

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