Joint event causality extraction using dual-channel enhanced neural network

Gao, J; Yu, H; Zhang, S

Joint event causality extraction using dual-channel enhanced neural network

Gao, J Yu, H

Zhang, S

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2022, 258
Issue Date:: 2022-12-22

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Field	Value	Language
dc.contributor.author	Gao, J
dc.contributor.author	Yu, H https://orcid.org/0000-0003-3444-9992
dc.contributor.author	Zhang, S
dc.date.accessioned	2023-03-21T01:45:21Z
dc.date.available	2023-03-21T01:45:21Z
dc.date.issued	2022-12-22
dc.identifier.citation	Knowledge-Based Systems, 2022, 258
dc.identifier.issn	0950-7051
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/167885
dc.description.abstract	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.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2022.109935
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Joint event causality extraction using dual-channel enhanced neural network
dc.type	Journal Article
utslib.citation.volume	258
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-21T01:45:20Z
pubs.publication-status	Published
pubs.volume	258

Abstract:

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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http://hdl.handle.net/10453/167885