@Article{cmc.2020.010870,
AUTHOR = {Shi Li, Xinyan Cao, Yiting Nan},
TITLE = {Multi-Level Feature-Based Ensemble Model for Target-Related  Stance Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {777--788},
URL = {http://www.techscience.com/cmc/v65n1/39594},
ISSN = {1546-2226},
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 introduce attention mechanisms 
to magnify target information-related features. Furthermore, we employ sparse coding to 
remove noise to obtain characteristic features. Performance was improved by using sparse 
coding on the basis of attention employment and feature extraction. We evaluate our 
approach on the SemEval-2016Task 6-A public dataset, achieving a performance that 
exceeds the benchmark and those of participating teams.},
DOI = {10.32604/cmc.2020.010870}
}