Abstract
In this paper, we presents a stance detection system for NLPCC-ICCPOL 2016 share task 4. Our Stance Detection System can determinate whether the author of Weibo text is in favor of the given target, against the given target, or neither. We exploit LSTMs model and the average F score of our system is 56.56%. In contrast to the traditional target/aspect sentiment, the given target may not be preserved in Weibo text. We model the task as a classification problem, exploiting LSTMs as the basic part of classifier.
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Acknowledgements
This study was supported by National Natural Science Foundation of China under Grants Nos. 61672211 and 61602160, the Natural Science Foundation of Heilongjiang Province under Grant No. F2016036, and the Returned Scholar Foundation of Heilongjiang Province, respectively.
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Yu, N., Pan, D., Zhang, M., Fu, G. (2016). Stance Detection in Chinese MicroBlogs with Neural Networks. 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_83
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DOI: https://doi.org/10.1007/978-3-319-50496-4_83
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