Abstract
Stance detection is the task of automatically determining the author’s favorability towards a given target. However, the target may not be explicitly mentioned in the text and even someone may refer some positive opinions to against the target, which make the task more difficult. In this paper, we describe an ensemble framework which integrates various feature sets and classification methods, and does not consist any handcrafted templates or rules to help stance detection. We submit our solution to NLPCC 2016 shared task: Detecting Stance in Chinese Weibo (Task A), which is a supervised task towards five targets. The official results show that our solution of the team “CBrain” achieves one 1st place and one 2nd place on these targets, and the overall ranking is 4th out of 16 teams. Our code is available at https://github.com/jacoxu/2016NLPCC_Stance_Detection.
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Acknowledgments
We thank the anonymous reviewers for their insightful comments, and this work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02070005), the National High Technology Research and Development Program of China (863 Program) (Grant No. 2015AA015402) and the National Natural Science Foundation (Grant No. 61602479 and 61403385).
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Xu, J., Zheng, S., Shi, J., Yao, Y., Xu, B. (2016). Ensemble of Feature Sets and Classification Methods for Stance Detection. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_61
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DOI: https://doi.org/10.1007/978-3-319-50496-4_61
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