Open Access
ARTICLE
Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection
Shi Li1, Xinyan Cao1, *, Yiting Nan2
1 School of Information Engineering, Northeast Forestry University, Harbin, China.
2 Petabase LLC, Washington DC, 20001, USA.
* Corresponding Author: Xinyan Cao. Email: .
Computers, Materials & Continua 2020, 65(1), 777-788. https://doi.org/10.32604/cmc.2020.010870
Received 02 April 2020; Accepted 09 June 2020; Issue published 23 July 2020
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.
Keywords
Cite This Article
S. Li, X. Cao and Y. Nan, "Multi-level feature-based ensemble model for target-related stance detection,"
Computers, Materials & Continua, vol. 65, no.1, pp. 777–788, 2020.
Citations