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A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based
doi:10.18653/v1/d19-1563
dblp:conf/emnlp/FanYDGBYXM19
fatcat:y3nvjoveofgxzn6bknqpwiqkfy