Text classification model based on CNN and BiGRU fusion attention mechanism
This model proposes a text classification model with deep learning algorithm, which combines the characteristics of Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) in cyclic neural network, extracts local and global features of text feature words respectively, and calculates the imp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02040.pdf |
| _version_ | 1811226828093784064 |
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| author | Ma Yuqun Chen Hailong Wang Qing Zheng Xin |
| author_facet | Ma Yuqun Chen Hailong Wang Qing Zheng Xin |
| author_sort | Ma Yuqun |
| collection | DOAJ |
| description | This model proposes a text classification model with deep learning algorithm, which combines the characteristics of Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) in cyclic neural network, extracts local and global features of text feature words respectively, and calculates the importance of words to text classification task after fusing attention mechanism (Attention). Make the model focus on the feature words with high weight. Through the fusion of models, the accuracy of text classification is improved. The experimental results on IMDB film review dataset, Fudan University Chinese dataset and THUCNews dataset show that the proposed model has different degrees of improvement compared with the previously proposed models based on CNN, or LSTM and related fusion models in terms of accuracy, recall rate and F1 value. |
| first_indexed | 2024-04-12T09:31:35Z |
| format | Article |
| id | doaj.art-adfb1d2d05be48bc9cb4aa9353a8af2d |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-12T09:31:35Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-adfb1d2d05be48bc9cb4aa9353a8af2d2022-12-22T03:38:21ZengEDP SciencesITM Web of Conferences2271-20972022-01-01470204010.1051/itmconf/20224702040itmconf_cccar2022_02040Text classification model based on CNN and BiGRU fusion attention mechanismMa Yuqun0Chen Hailong1Wang Qing2Zheng Xin3School of Computer Science and Technology, Harbin University of Science and TechnologySchool of Computer Science and Technology, Harbin University of Science and TechnologySchool of Computer Science and Technology, Harbin University of Science and TechnologySchool of Computer Science and Technology, Harbin University of Science and TechnologyThis model proposes a text classification model with deep learning algorithm, which combines the characteristics of Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) in cyclic neural network, extracts local and global features of text feature words respectively, and calculates the importance of words to text classification task after fusing attention mechanism (Attention). Make the model focus on the feature words with high weight. Through the fusion of models, the accuracy of text classification is improved. The experimental results on IMDB film review dataset, Fudan University Chinese dataset and THUCNews dataset show that the proposed model has different degrees of improvement compared with the previously proposed models based on CNN, or LSTM and related fusion models in terms of accuracy, recall rate and F1 value.https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02040.pdftext categorizationdeep learningconvolution neural network (cnn)gate recurrent unit (gru)attention |
| spellingShingle | Ma Yuqun Chen Hailong Wang Qing Zheng Xin Text classification model based on CNN and BiGRU fusion attention mechanism ITM Web of Conferences text categorization deep learning convolution neural network (cnn) gate recurrent unit (gru) attention |
| title | Text classification model based on CNN and BiGRU fusion attention mechanism |
| title_full | Text classification model based on CNN and BiGRU fusion attention mechanism |
| title_fullStr | Text classification model based on CNN and BiGRU fusion attention mechanism |
| title_full_unstemmed | Text classification model based on CNN and BiGRU fusion attention mechanism |
| title_short | Text classification model based on CNN and BiGRU fusion attention mechanism |
| title_sort | text classification model based on cnn and bigru fusion attention mechanism |
| topic | text categorization deep learning convolution neural network (cnn) gate recurrent unit (gru) attention |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02040.pdf |
| work_keys_str_mv | AT mayuqun textclassificationmodelbasedoncnnandbigrufusionattentionmechanism AT chenhailong textclassificationmodelbasedoncnnandbigrufusionattentionmechanism AT wangqing textclassificationmodelbasedoncnnandbigrufusionattentionmechanism AT zhengxin textclassificationmodelbasedoncnnandbigrufusionattentionmechanism |