Text Classification using Capsules
This work addresses text classification for NLP researchers, but it is incremental as it adapts an existing method to a new domain without major breakthroughs.
The paper tackled text classification by exploring capsule networks, showing they have potential and advantages over convolutional neural networks, with a proposed routing method reducing computational complexity and achieving comparable results on seven benchmark datasets.
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.