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Tree-based Label Dependency Topic Models

Multi-labeled corpora, where each document is tagged with a set of labels, are ubiquitous. When the number of unique labels in the dataset is large, there are naturally some dependencies among the labels. In this paper, we propose TREELAD—a hierarchical topic model to capture these label dependencies using a tree-structured topic hierarchy. We apply TREELAD on a real-world dataset and show some promising empirical results.