Joint event causality extraction using dual-channel enhanced neural network
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- Knowledge-Based Systems, 2022, 258
- Issue Date:
- 2022-12-22
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Joint event causality extraction using dual-channel enhanced neural network.pdf | Published version | 1.67 MB |
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Event Causality Extraction (ECE) plays an essential role in many Natural Language Processing (NLP), such as event prediction and dialogue generation. Recent research in NLP treats ECE as a sequence labeling problem. However, these methods tend to extract the events and their relevant causality using a single collapsed model, which usually focuses on the textual contents while ignoring the intra-element transitions inside events and inter-event causality transition association across events. In general, ECE should condense the complex relationship of intra-event and the causality transition association among events. Therefore, we propose a novel dual-channel enhanced neural network to address this limitation by taking both global event mentions and causality transition association into account. To extract complete event mentions, a Textual Enhancement Channel(TEC) is constructed to learn important intra-event features from the training data with a wider perception field. Then the Knowledge Enhancement Channel(KEC) incorporates external causality transition knowledge using a Graph Convolutional Network (GCN) to provide complementary information on event causality. Finally, we design a dynamic fusion attention mechanism to measure the importance of the two channels. Thus, our proposed model can incorporate both semantic-level and knowledge-level representations of events to extract the relevant event causality. Experimental results on three public datasets show that our model outperforms the state-of-the-art methods.
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