Open AccessWords in Pairs Neural Networks for Text Classification
Author(s) -
WU Yujia,
LI Jing,
SONG Chengfang,
CHANG Jun
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.03.005
Subject(s) - word (group theory) , computer science , artificial intelligence , artificial neural network , feature (linguistics) , natural language processing , benchmark (surveying) , pattern recognition (psychology) , mathematics , linguistics , philosophy , geometry , geodesy , geography
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.