Hierarchical Deep Document Model

Topic modeling is a commonly used text analysis tool for discovering latent topics in a text corpus. However, while topics in a text corpus often exhibit a hierarchical structure (e.g., cellphone is a sub-topic of electronics), most topic modeling methods assume a flat topic structure that ignores the hierarchical dependency among topics, or utilize a predefined topic hierarchy. In this work, we present a novel Hierarchical Deep Document Model (HDDM) to learn topic hierarchies using a variational autoencoder framework. We propose a novel objective function, sum of log likelihood, instead of the widely used evidence lower bound, to facilitate the learning of hierarchical latent topic structure. The proposed objective function can directly model and optimize the hierarchical topic-word distributions at all topic levels. We conduct experiments on four real-world text datasets to evaluate the topic modeling capability of the proposed HDDM method compared to state-of-the-art hierarchical topic modeling benchmarks. Experimental results show that HDDM achieves considerable improvement over benchmarks and is capable of learning meaningful topics and topic hierarchies. To further demonstrate the practical utility of HDDM, we apply it to a real-world medical notes dataset for clinical prediction. Experimental results show that HDDM can better summarize topics in medical notes, resulting in more accurate clinical predictions.