In this paper, we explore the feature space in the web person name disambiguation task and propose a topic-based model which exploits corpus level person specific evidences to handle the data sparsity challenges, especially the case that limited evidences can be collected from the local context.
we harvest topics from wikipedia concepts appearing in the name observation set, and weight a concept based on both the relatedness of the concept to its corresponding topic and the importance of this topic in the current name observation set, so that some discriminative but sparse features can obtain more reliable weights.