Chinese text classification based on character-level CNN and SVM Online publication date: Fri, 11-Oct-2019
by Huaiguang Wu; Daiyi Li; Ming Cheng
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 12, No. 3, 2019
Abstract: Aiming at the problems of curse of dimensionality, sparse data and long computation time in traditional SVM classification algorithm based on term frequency-inverse document frequency (TF-IDF), we propose a novel hybrid system for Chinese text classification: CSVM, which is independent of the hand-designed features and domain knowledge. Firstly, the encoding words are done by constructing a text vocabulary of size m for the input language, and then quantise each word using 1-of-m encoding. Secondly, we exploit the convolutional neural network (CNN) to extract the morphological features of character vectors from each word, and then through large scale text material training the semantic feature of each word vectors are be obtained the semantic feature of each word vectors. Finally, the text classification is carried out with the SVM multiple classifier. The experimental results show that the CSVM algorithm is more effective than other traditional Chinese text classification algorithm.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com