Tag recommendation by text classification with attention-based capsule network

Abstract

Tag recommendation has been attracting much attention with the growth of digital resources. The goal of a tag recommendation system is to provide a set of tags for a piece of text to ease the tagging process done manually by a user. These tags have been shown to enhance the capabilities of search engines for navigating, organizing and searching content. However, tag text manually is time-consuming and labor-intensive. In this paper, we introduce a tag recommendation by text classification. We explore the capsule network with dynamic routing for the tag recommendation task. The capsule network encodes the intrinsic spatial relationship between a part and a whole constituting viewpoint invariant knowledge that automatically generalizes to novel viewpoints. In addition, an attention mechanism is incorporated into the capsule network to distill important information from the input documents. We conduct extensive experiments on large publication datasets to evaluate the effectiveness of our model. The experimental results demonstrate that our model substantially outperforms the compared baseline methods and achieves the state-of-the-art results.