Title: Causal relation extraction and network construction of web events

Authors: Qichen Ma

Addresses: School of Computer Engineering and Science, Shanghai University, Shanghai, China

Abstract: The explosive increase of news data on the web has created a mass of causal knowledge, which explains the causal relation between two events that effect event will occur following the occurrence of cause event. Analysis of causal knowledge has gain lots of attentions due to its widespread applications, such as question answering, event prediction, generating future scenarios, and commonsense causal reasoning. However, few researches are based on Chinese news corpus, and no effective causal template is proposed for extracting Chinese causal relationship. Therefore, the method for extracting causal relation and building network of causal events from Chinese news corpus is proposed. First, we propose a method to obtain complete cue phrases set and present four common causal patterns to extract causal relations. And then we merge the same events by similarity calculation of causal events. At last, a network of causal events is constructed. Experiments on the datasets show the effectiveness of the proposed approach.

Keywords: causality knowledge; causal relation extraction; causality network.

DOI: 10.1504/IJSHC.2019.101592

International Journal of Social and Humanistic Computing, 2019 Vol.3 No.2, pp.135 - 147

Received: 25 Jul 2018
Accepted: 14 Aug 2018
Published online: 13 Aug 2019 *

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