Retrieving semantic similar short texts is a crucial issue to many applications, e.g., web search, ads matching, question-answer system, and so forth. Most of the traditional methods concentrate on how to improve the precision of the similarity measurement, while current real applications need to efficiently explore the top similar short texts semantically related to the query one. We address the efficiency issue in this paper by investigating the similarity strategies and incorporating them into the FAST framework (efficient FrAmework for semantic similar Short Texts retrieval). We conduct comprehensive performance evaluation on real-life data which shows that our proposed method outperforms the state-of-the-art techniques.
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