September 10, 2025電腦學刊

Leveraging Causal Convolutional Neural Networks and Knowledge Matching for Enhanced Event Causality Identification

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The method significantly improves event causality identification by effectively utilizing causal word cues.
Using a convolutional neural network, the model extracts causal relationships between events based on knowledge matching.
Experimental results show that this approach outperforms previous methods on two widely used datasets.
Causal indicators from FrameNet provide a robust initialization for the convolutional filter in this novel framework.

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This framework enhances event causality identification by leveraging causal word cues and external knowledge for improved accuracy.

Wang et al. (2025) studied this question.

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