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13 May 2022 Hierarchical convolutional recurrent neural network for Chinese text classification
Zhifeng Ma, Shuaibo Li, Hao Zhang, Li Li, Jie Liu
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Proceedings Volume 12248, Second International Conference on Sensors and Information Technology (ICSI 2022); 122480Z (2022) https://doi.org/10.1117/12.2637506
Event: 2nd International Conference on Sensors and Information Technology (ICSI 2022), 2022, Nanjing, China
Abstract
Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Previous models assume that all classes are equally difficult to distinguish and treat all of them exclusively. But in fact, the property of general-to-specific category ordering often exists between classes. In this paper, we exploit the prior knowledge of class hierarchical structure to enforce the network to learn human-understandable concepts in different blocks and propose a new model named H-CRNN, which combines TextCNN and Bi-LSTM to construct a hierarchical structure. We test our proposed model on the THUCNews dataset, and experiments show that our proposed H-CRNN model achieves the best results than other methods.
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Zhifeng Ma, Shuaibo Li, Hao Zhang, Li Li, and Jie Liu "Hierarchical convolutional recurrent neural network for Chinese text classification", Proc. SPIE 12248, Second International Conference on Sensors and Information Technology (ICSI 2022), 122480Z (13 May 2022); https://doi.org/10.1117/12.2637506
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KEYWORDS
Classification systems
Convolution
Data modeling
Neural networks
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