期刊
CHINESE JOURNAL OF ELECTRONICS
卷 29, 期 3, 页码 491-500出版社
WILEY
DOI: 10.1049/cje.2020.03.005
关键词
Data mining; Neural networks; High utility itemset; Text classification; Words in pairs
资金
- National Natural Science Foundation of China [61772180, 41201404]
- Fundamental Research Funds for the Central Universities of China [2042015gf0009]
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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