WU Yujia, LI Jing, SONG Chengfang, et al., “Words in Pairs Neural Networks for Text Classification,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 491-500, 2020, doi: 10.1049/cje.2020.03.005
Citation: WU Yujia, LI Jing, SONG Chengfang, et al., “Words in Pairs Neural Networks for Text Classification,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 491-500, 2020, doi: 10.1049/cje.2020.03.005

Words in Pairs Neural Networks for Text Classification

doi: 10.1049/cje.2020.03.005
Funds:  This work is supported by National Natural Science Foundation of China (No.61772180, No.41201404) and Fundamental Research Funds for the Central Universities of China (No.2042015gf0009).
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  • Corresponding author: LI Jing (corresponding author) was born in 1967. He received the Ph.D. degree from Wuhan University, Wuhan, China, in 2006. He is currently a Professor in Computer School of Wuhan University, Wuhan, China. His research interests include data mining and multimedia technology. (Email:leejingcn@whu.edu.cn)
  • Received Date: 2018-12-25
  • Rev Recd Date: 2020-01-09
  • Publish Date: 2020-05-10
  • Existing methods utilized single words as text features. Some words contain multiple meanings, and it is difficult to distinguish its specific classification according to a single word, which probably affects the accuracy of the text classification. Propose a framework based on Words in pairs neural networks (WPNN) for text classification. Words in pairs include all single word combinations which have a high mutual association. Mine the crucial explicit and implicit Words in pairs as text features. These words in pairs as a text feature are easily classified. The words in pairs are utilized as the input of the neural network, which provides a better classification ability to the model, because they are more recognizable than the single word. Experimental results show that our model outperforms five benchmark algorithms.
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