  Since the length of microblog texts, such as tweets, is strictly limited to
140 characters, traditional Information Retrieval techniques suffer from the
vocabulary mismatch problem severely and cannot yield good performance in the
context of microblogosphere. To address this critical challenge, in this paper,
we propose a new language modeling approach for microblog retrieval by
inferring various types of context information. In particular, we expand the
query using knowledge terms derived from Freebase so that the expanded one can
better reflect users' search intent. Besides, in order to further satisfy
users' real-time information need, we incorporate temporal evidences into the
expansion method, which can boost recent tweets in the retrieval results with
respect to a given topic. Experimental results on two official TREC Twitter
corpora demonstrate the significant superiority of our approach over baseline
methods.
