Words in Pairs Neural Networks for Text Classification. Issue 3 (1st May 2020)

• Record Type: Journal Article
• Authors: WU, Yujia; LI, Jing; SONG, Chengfang; CHANG, Jun
• Is Part Of: Chinese journal of electronics. Volume 29:Issue 3(2020)
• Page Start: 491
• Page End: 500
• Publication Date: 2020-05-01
• Subjects: classification -- image classification -- neural nets -- pattern classification -- text analysis; implicit Words -- crucial explicit Words -- single word combinations -- pairs neural networks -- text classification -- specific classification -- text feature; Data mining -- Neural networks -- High utility itemset -- Text classification -- Words in pairs; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2020.03.005
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.