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A DCRC Model for Text Classification
Resource type
Authors/contributors
- Hao, Zhaoquan (Author)
- Jin, Jiangyong (Author)
- Liang, Shengbin (Author)
- Cheng, Suying (Author)
- Shen, Yanqing (Author)
- Lee, Roger (Editor)
Title
A DCRC Model for Text Classification
Abstract
Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.
Book Title
Computer and Information Science
Series
Studies in Computational Intelligence
Place
Cham
Publisher
Springer International Publishing
Date
2023
Pages
85-99
Language
en
ISBN
978-3-031-12127-2
Accessed
4/11/23, 1:13 PM
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Citation
Hao, Z., Jin, J., Liang, S., Cheng, S., & Shen, Y. (2023). A DCRC Model for Text Classification. In R. Lee (Ed.), Computer and Information Science (pp. 85–99). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_6
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