Statistical and Semantic Approaches for Tweet Contextualization

Meriem Amina Zingla (1) , Latiri Chiraz (1) , Yahya Slimani (1) , Catherine Berrut (1)

1 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]

Microblogging sites, like Twitter, have emerged as a popular platform for expressing opinions. Bound to 140 characters, Twitter's publications (tweets) are very short and not always written maintaining formal grammar and proper spelling. The spelling variations increase the likelihood of vocabulary mismatch and make the tweets dicult to understand without some kind of context. The task of tweet contextualization was organized around these issues. It aims to provide an automatic readable context explaining a given tweet, in order to help the reader understand this latter. In this paper, we describe statistical and semantic approaches for the tweet contextualization task. While the statistical one is based on association rules mining, the semantic one uses Wikipedia as an external knowledge source. The e↵ectiveness of our approaches is proved through an experimental study conducted on the INEX 2013 collection.

Mots clés: Association rules; Informativeness evaluation; Wikipedia; Twitter; Tweet contextualization track; Query expansion

Domaines: Recherche d'information [cs.IR]

Meriem Amina Zingla, Latiri Chiraz, Yahya Slimani, Catherine Berrut. Statistical and Semantic Approaches for Tweet Contextualization. 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, National University of Singapore Institute of System SCience (NUS-ISS), Sep 2015, Singapour, Singapore. ⟨hal-01145991⟩