Chinese News Text Classification Method via Key Feature Enhancement
(1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently org...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/9/5399 |
| _version_ | 1797603058719391744 |
|---|---|
| author | Bin Ge Chunhui He Hao Xu Jibing Wu Jiuyang Tang |
| author_facet | Bin Ge Chunhui He Hao Xu Jibing Wu Jiuyang Tang |
| author_sort | Bin Ge |
| collection | DOAJ |
| description | (1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. It is noted that the existing deep learning methods rely on a large-scale tagged corpus for news text classification tasks and this model is poorly interpretable because the size is large. (2) Methods: To solve the above problems, this paper proposes a Chinese news text classification method based on key feature enhancement named KFE-CNN. It can effectively expand the semantic information of key features to enhance sample data and then combine the zero–one binary vector representation to transform text features into binary vectors and input them into CNN model for training and implementation, thus improving the interpretability of the model and effectively compressing the size of the model. (3) Results: The experimental results show that our method can significantly improve the overall performance of the model and the average accuracy and F<sub>1</sub>-score of the THUCNews subset of the public dataset reached 97.84% and 98%. (4) Conclusions: this fully proved the effectiveness of the KFE-CNN method for the Chinese news text classification task and it also fully demonstrates that key feature enhancement can improve classification performance. |
| first_indexed | 2024-03-11T04:24:09Z |
| format | Article |
| id | doaj.art-551784b8dd0940c0824b00abd71dc6ee |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T04:24:09Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-551784b8dd0940c0824b00abd71dc6ee2023-11-17T22:33:28ZengMDPI AGApplied Sciences2076-34172023-04-01139539910.3390/app13095399Chinese News Text Classification Method via Key Feature EnhancementBin Ge0Chunhui He1Hao Xu2Jibing Wu3Jiuyang Tang4Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, ChinaLaboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, ChinaLaboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, ChinaLaboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, ChinaLaboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China(1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. It is noted that the existing deep learning methods rely on a large-scale tagged corpus for news text classification tasks and this model is poorly interpretable because the size is large. (2) Methods: To solve the above problems, this paper proposes a Chinese news text classification method based on key feature enhancement named KFE-CNN. It can effectively expand the semantic information of key features to enhance sample data and then combine the zero–one binary vector representation to transform text features into binary vectors and input them into CNN model for training and implementation, thus improving the interpretability of the model and effectively compressing the size of the model. (3) Results: The experimental results show that our method can significantly improve the overall performance of the model and the average accuracy and F<sub>1</sub>-score of the THUCNews subset of the public dataset reached 97.84% and 98%. (4) Conclusions: this fully proved the effectiveness of the KFE-CNN method for the Chinese news text classification task and it also fully demonstrates that key feature enhancement can improve classification performance.https://www.mdpi.com/2076-3417/13/9/5399key featuretext classificationdata augmentationKFE-CNNneural network |
| spellingShingle | Bin Ge Chunhui He Hao Xu Jibing Wu Jiuyang Tang Chinese News Text Classification Method via Key Feature Enhancement Applied Sciences key feature text classification data augmentation KFE-CNN neural network |
| title | Chinese News Text Classification Method via Key Feature Enhancement |
| title_full | Chinese News Text Classification Method via Key Feature Enhancement |
| title_fullStr | Chinese News Text Classification Method via Key Feature Enhancement |
| title_full_unstemmed | Chinese News Text Classification Method via Key Feature Enhancement |
| title_short | Chinese News Text Classification Method via Key Feature Enhancement |
| title_sort | chinese news text classification method via key feature enhancement |
| topic | key feature text classification data augmentation KFE-CNN neural network |
| url | https://www.mdpi.com/2076-3417/13/9/5399 |
| work_keys_str_mv | AT binge chinesenewstextclassificationmethodviakeyfeatureenhancement AT chunhuihe chinesenewstextclassificationmethodviakeyfeatureenhancement AT haoxu chinesenewstextclassificationmethodviakeyfeatureenhancement AT jibingwu chinesenewstextclassificationmethodviakeyfeatureenhancement AT jiuyangtang chinesenewstextclassificationmethodviakeyfeatureenhancement |