Text classification using capsules.

This paper presents an empirical exploration of the use of capsule networks for text classification. While it has been shown that capsule networks are effective for image classification, the research regarding their validity in the domain of text has been initiated recently. In this paper, we show that capsule networks indeed have the potential for text classification and that they have several advantages over convolutional neural networks. As well, we compare our proposed model to the initial studies regarding capsule network-based text classification. We further suggest a simple routing method that effectively reduces the computational complexity of dynamic routing. We utilized seven benchmark datasets to demonstrate that capsule networks, along with the proposed routing method provide comparable results. [ABSTRACT FROM AUTHOR]