Causal event extraction using causal event element-oriented neural network Online publication date: Tue, 04-Jan-2022
by Kai Xu; Peng Wang; Xue Chen; Xiangfeng Luo; Jianqi Gao
International Journal of Computational Science and Engineering (IJCSE), Vol. 24, No. 6, 2021
Abstract: Causal event extraction plays an important role in natural language processing (NLP) such as question answering, decision making and event prediction. Previous work extracts causal events using template-matching methods, machine-learning methods, or deep-learning methods. However, these methods ignore the guiding role of specific causal patterns on causal event extraction. In this paper, we propose causal event element-oriented neural network CEEONN to extract causal events. Firstly, we construct causal event element knowledge base CEEKB from domain casual text. Then we construct a neural network by incorporating both the entire sentence and associated causal patterns into a better semantic representation. With domain-based CEEKB, the proposed CEEONN can be better guided to identify specific causal patterns. Experiments show that CEEONN achieves competitive results compared with previous work.
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