Trending topics in microblogs such as Twitter are valuable resources to under-stand social aspects of real-world events. To enable deep analyses of such trends, se-mantic annotation is an effective approach; yet the problem of annotating microblog trending topics is largely unexplored by the research community. In this work, we tackle the problem of mapping trending Twitter topics to entities from Wikipedia. We propose a novel model that comple-ments traditional text-based approaches by rewarding entities that exhibit a high tem-poral correlation with topics during their burst time period. By exploiting temporal information from the Wikipedia edit his-tory and page view logs, we have improved the annotation performance by 17-28%, as compared to the competitive baselines.
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