Character-level Text Classification Via Convolutional Neural Network and Gated Recurrent Unit
International Journal of Machine Learning and Cybernetics(2020)
China University of Mining and Technology
Abstract
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.
MoreTranslated text
Key words
Text categorization,Convolutional neural network,Gated recurrent unit,Highway network
PDF
View via Publisher
AI Read Science
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined