TY - CHAP TI - A DCRC Model for Text Classification AU - Hao, Zhaoquan AU - Jin, Jiangyong AU - Liang, Shengbin AU - Cheng, Suying AU - Shen, Yanqing T2 - Computer and Information Science A2 - Lee, Roger T3 - Studies in Computational Intelligence AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 85 EP - 99 LA - en PB - Springer International Publishing SN - 978-3-031-12127-2 UR - https://doi.org/10.1007/978-3-031-12127-2_6 Y2 - 2023/04/11/13:13:10 KW - BiGRU KW - BiLSTM KW - CNN KW - Text classification ER -