Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, Yulan He. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

In this paper, we present our work in emotion cause extraction.Since there is no open dataset available, the lack of annotated resources has limited the research in this area.Thus, we first present a dataset we built using SINA city news.The annotation is based on the scheme of the W3C Emotion Markup Language.Second, we propose a 7-tuple definition to describe emotion cause events.Based on this general definition, we propose a new event-driven emotion cause extraction method using multi-kernel SVMs where a syntactical tree based approach is used to represent events in text.A convolution kernel based multikernel SVM are used to extract emotion causes.Because traditional convolution kernels do not use lexical information at the terminal nodes of syntactic trees, we modify the kernel function with a synonym based improvement.Even with very limited training data, we can still extract sufficient features for the task.Evaluations show that our approach achieves 11.6% higher F-measure compared to referenced methods.The contributions of our work include resource construction, concept definition and algorithm development.