Compositional coding capsule network with k-means routing for text classification
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Text classification is a challenging problem which aims to identify the category of text. Recently, Capsule Networks (CapsNets) are proposed for image classification, it has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while, their validity in the domain of text has less been explored. An effective method named deep compositional code learning has been proposed lately. This method can save many parameters about word embeddings without any significant sacrifices in performance. In this paper, we introduce the Compositional Coding (CC) mechanism between capsules, and we propose a new routing algorithm, which is based on k-means clustering theory. Experiments conducted on eight challenging text classification datasets show the proposed method achieves similar accuracy compared to the state-of-the-art approach with significantly fewer parameters.
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