  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, their validity in the domain of text has
not been explored. 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. 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.
