Semi-supervised text classification with deep convolutional neural network using feature fusion approach
parvaneh shayegh, Yuefeng Li, Jinglan Zhang, Qing Zhang · IEEE/WIC/ACM International Conference on Web Intelligence · 2019
Supervised learning algorithms employ labeled training data for classification purposes while obtaining labeled data for large datasets is costly and time consuming. Semi-supervised learning algorithms, on the contrary, use a small set of labeled data and a large set of unlabeled data to improve predication performance and thus may be a good alternative to supervised learning algorithms for large text datasets. Although many semi-supervised learning algorithms have been proposed in the data science literature, most of these algorithms are not feasible for discrete and unstructured text data.