Gaigai Jin1, Junsheng Zhou1,*, Weiguang Qu1, Yunfei Long2, Yanhui Gu1
Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 161-173, 2021, DOI:10.32604/iasc.2021.017440
- 26 July 2021
Abstract Event causality identification is an essential task for information extraction that has attracted growing attention. Early researchers were accustomed to combining the convolutional neural network or recurrent neural network models with external causal knowledge, but these methods ignore the importance of rich semantic representation of the event. The event is more structured, so it has more abundant semantic representation. We argue that the elements of the event, the interaction of the two events, and the context between the two events can enrich the event’s semantic representation and help identify event causality. Therefore, the effective semantic… More >
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