Open AccessMulti‐channel Sliced Deep RCNN with Residual Network for Text Classification
Author(s) -
Zhou Chuanhua,
Zhou Jiayi,
Yu Cai,
Zhao Wei,
Pan Ruilin
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.2020.08.003
Subject(s) - bigram , residual , computer science , artificial intelligence , convolutional neural network , deep learning , pattern recognition (psychology) , artificial neural network , machine learning , algorithm , trigram
We propose a multi‐channel sliced deep Recurrent convolutional neural network (RCNN) with a residual network. We expand the RCNN into a deep neural network. Our proposed model can directly learn to extract bigram features and other features from sentences where other machine learning methods cannot. The experimental results indicate that our model outperforms the traditional methods.