Title: Causal event extraction using causal event element-oriented neural network

Authors: Kai Xu; Peng Wang; Xue Chen; Xiangfeng Luo; Jianqi Gao

Addresses: School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China; State Key Laboratory of Mathematical Engineering and Advanced Computing, No. 699 East Shanshui Road, WuXi, China; Shanghai Si-future Electronic Science and Technology Company Limited., No. 661 Dengmin Road, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China

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.

Keywords: causal event elements; causal event extraction; causal patterns.

DOI: 10.1504/IJCSE.2021.119985

International Journal of Computational Science and Engineering, 2021 Vol.24 No.6, pp.621 - 628

Received: 05 Nov 2020
Accepted: 23 Feb 2021
Published online: 04 Jan 2022 *

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