VPALG

Abstract

Paper-publication venue prediction aims to predict candidate publication venues that effectively suit given submissions. This technology is developing rapidly with the popularity of machine learning models. However, most previous methods ignore the structure information of papers, while modeling them with graphs can naturally solve this drawback. Meanwhile, they either use hand-crafted or bag-of-word features to represent the papers, ignoring the ones that involve high-level semantics. Moreover, existing methods assume that the venue where a paper is published as a correct venue for the data annotation, which is unrealistic. One paper can be relevant to many venues. In this paper, we attempt to address these problems above and develop a novel prediction model, namelyVenue Prediction with Abstract-Level Graph (Vpalg xspace), which can serve as an effective decision-making tool for venue selections. Specifically, to achieve more discriminative paper abstract representations, we construct each abstract as a semantic graph and perform a dual attention message passing neural network for representation learning. Then, the proposed model can be trained over the learned abstract representations with their labels and generalized via self-training. Empirically, we employ the PubMed dataset and further collect two new datasets from the top journals and conferences in computer science. Experimental results indicate the superior performance of Vpalg xspace, consistently outperforming the existing baseline methods.