Abstract
Emotion cause detection plays a key role in many downstream sentiment analysis applications. Research shows that both knowledge from data and experience from linguistics help to improve the detection performance. In this paper, we propose an approach to combine them. We utilize a hierarchical framework to model emotional texts, in which the emotion is independently represented along with each word and clause in a document. We also employ linguistic features to help the model find the emotion cause. Such features by manual work help to describe deep semantic relations between emotions and their causes that are difficult to be cast by representation models. Experimental results show that the combination model helps to detect emotion cause within emotional texts with complex semantic relations.
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Acknowledgements
This work is supported by the Foundation of the Guangdong 13th Five-year Plan of Philosophy and Social Sciences (GD20XZY01, GD19CYY05), the General Project of National Scientific and Technical Terms Review Committee (YB2019013), the Special Innovation Project of Guangdong Education Department (2017KTSCX064), the Graduate Research Innovation Project of Guangdong University of Foreign Studies(21GWCXXM-068), and the Bidding Project of GDUFS Laboratory of Language Engineering and Computing (LEC2019ZBKT002).
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Wan, J., Ren, H. (2022). Emotion Cause Detection with a Hierarchical Network. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_53
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DOI: https://doi.org/10.1007/978-981-16-2380-6_53
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