TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
CNN-based Skip-Gram Method for Improving Classification Accuracy of Chinese Text |
| 영문제목(English Title) |
CNN-based Skip-Gram Method for Improving Classification Accuracy of Chinese Text |
| 저자(Author) |
Wenhua Xu
Hao Huang
Jie Zhang
Hao Gu
Jie Yang
Guan Gui
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| 원문수록처(Citation) |
VOL 13 NO. 12 PP. 6080 ~ 6096 (2019. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Text classification is one of the fundamental techniques in natural language processing. Numerous studies are based on text classification, such as news subject classification, question answering system classification, and movie review classification. Traditional text classification methods are used to extract features and then classify them. However, traditional methods are too complex to operate, and their accuracy is not sufficiently high. Recently, convolutional neural network (CNN) based one-hot method has been proposed in text classification to solve this problem. In this paper, we propose an improved method using CNN based skip-gram method for Chinese text classification and it conducts in Sogou news corpus. Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.
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| 키워드(Keyword) |
Natural language processing (NLP)
deep learning
text classification
convolutional neural networks
skip-gram method
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| 파일첨부 |
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