Abstract
A large-scale training corpus consisting of microblogs belonging to a desired category is important for high-accuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and labor-consuming. Therefore, some models for the automatic retrieval of microblogs froman exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.
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