Character-level text classification via convolutional neural network and gated recurrent unit

Text categorization, or text classification, is one of key tasks for representing the semantic information of documents. Traditional deep leaning models for text categorization are generally time-consuming on large scale datasets due to slow convergence rate or heavily rely on the pre-trained word vectors. Motivated by fully convolutional networks in the field of image processing, we introduce fully convolutional layers to substantially reduce the number of parameters in the text classification model. A character-level model for short text classification, integrating convolutional neural network, bidirectional gated recurrent unit, highway network with the fully connected layers, is proposed to capture both the global and the local textual semantics at the fast convergence speed. Furthermore, In addition, error minimization extreme learning machine is incorporated into the proposed model to improve the classification accuracy further. Extensive experiments show that our approach achieves the state-of-the-art performance compared with the existing methods on the large scale text datasets.