Therefore, we propose to train convolutional neural networks (CNNs) to generalize knowledge from the preliminary predictions given by the embedding space.
we first pre-train CNNs with soft predictions given by the cosine similarity between document embeddings and topic embeddings, and then adopt a self-training strategy to further refine the CNNs using their highconfident predictions on unlabeled documents.