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Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics’ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose...
Topic model has attracted much attention from investigators, as it provides users with insights into the huge volumes of documents. However, most previous related studies that based on Non-negative Matrix Factorization (NMF) neglect to figure out which topics are widespread in the documents and which are not. These widespread topics, which we refer to coarse-grained topics, have great significance...
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