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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: email.

Computers, Materials & Continua 2020, 65(1), 777-788. https://doi.org/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 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.

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APA Style
Li, S., Cao, X., Nan, Y. (2020). Multi-level feature-based ensemble model for target-related stance detection. Computers, Materials & Continua, 65(1), 777-788. https://doi.org/10.32604/cmc.2020.010870
Vancouver Style
Li S, Cao X, Nan Y. Multi-level feature-based ensemble model for target-related stance detection. Comput Mater Contin. 2020;65(1):777-788 https://doi.org/10.32604/cmc.2020.010870
IEEE Style
S. Li, X. Cao, and Y. Nan "Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection," Comput. Mater. Contin., vol. 65, no. 1, pp. 777-788. 2020. https://doi.org/10.32604/cmc.2020.010870

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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