Abstract
Twitter has emerged as the most popular among microblogging service providers. The content provided in Twitter is large, diverse, and huge in quantity. Given the increasing amount of information available through such microblogging sites, it would be interesting to be able to retrieve useful tweets in response to a given information need. However, Twitter's subscribers often have many difficulties dealing with its content. Especially in searching for tweets that satisfy their information needs. This problem becomes more complicated when the user-defined queries are short and precise. This paper deals with short and precise queries problem for micoblog retrieval. We expand short queries by semantically related terms extracted from Wikipedia, DBpedia and unstructured texts using textmining techniques. Experiments on TREC 2011 microblog collection show significant improvement in the retrieval performance.