Shi Li1, Xinyan Cao1, *, Yiting Nan2
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 777-788, 2020, DOI:10.32604/cmc.2020.010870
Abstract Stance detection is the task of attitude identification toward a standpoint. Previous
work of stance detection has focused on feature extraction but ignored the fact that irrelevant
features exist as noise during higher-level abstracting. Moreover, because the target is not
always mentioned in the text, most methods have ignored target information. In order to
solve these problems, we propose a neural network ensemble method that combines the
timing dependence bases on long short-term memory (LSTM) and the excellent extracting
performance of convolutional neural networks (CNNs). The method can obtain multi-level
features that consider both local and global features. We also… More >
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