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Future Internet 2018, 10(11), 113; https://doi.org/10.3390/fi10110113

Chinese Text Classification Model Based on Deep Learning

School of Computer Science and Technology, Donghua University, Shanghai 201620, China
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Received: 18 October 2018 / Revised: 12 November 2018 / Accepted: 12 November 2018 / Published: 20 November 2018
(This article belongs to the Special Issue Innovative Topologies and Algorithms for Neural Networks)
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Abstract

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts. View Full-Text
Keywords: Chinese text classification; long short-term memory; convolutional neural network Chinese text classification; long short-term memory; convolutional neural network
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Li, Y.; Wang, X.; Xu, P. Chinese Text Classification Model Based on Deep Learning. Future Internet 2018, 10, 113.

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