From text to graph: a general transition-based AMR parsing using neural network

Gu, M; Gu, Y; Luo, W; Xu, G; Yang, Z; Zhou, J; Qu, W

From text to graph: a general transition-based AMR parsing using neural network

Gu, M Gu, Y Luo, W Xu, G

Yang, Z Zhou, J Qu, W

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2021, 33, (11), pp. 6009-6025
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Gu, M
dc.contributor.author	Gu, Y
dc.contributor.author	Luo, W
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.contributor.author	Yang, Z
dc.contributor.author	Zhou, J
dc.contributor.author	Qu, W
dc.date.accessioned	2021-07-26T11:48:23Z
dc.date.available	2021-07-26T11:48:23Z
dc.date.issued	2021
dc.identifier.citation	Neural Computing and Applications, 2021, 33, (11), pp. 6009-6025
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/149939
dc.description.abstract	© 2020, Springer-Verlag London Ltd., part of Springer Nature. Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing, which tells the inner meaning of language form. Recently, Abstract Meaning Representation (AMR) is attracted by many researchers for its semantic representation ability on an entire sentence. However, due to the non-projectivity and reentrancy properties of AMR graphs, they lose some important semantic information in parsing from sentences. In this paper, we propose a general AMR parsing model which utilizes a two-stack-based transition algorithm for both Chinese and English datasets. It can incrementally parse sentences to AMR graphs in linear time. Experimental results demonstrate that it is superior in recovering reentrancy and handling arcs while is competitive with other transition-based neural network models on both English and Chinese datasets.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-020-05378-5
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Neural Computing and Applications, 2021, 33, (11), pp. 6009-6025]. The final authenticated version is available online at: [ https://link.springer.com/article/10.1007%2Fs00521-020-05378-5]”
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	From text to graph: a general transition-based AMR parsing using neural network
dc.type	Journal Article
utslib.citation.volume	33
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 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/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	recently_added	*
pubs.consider-herdc	false
dc.date.updated	2021-07-26T11:48:19Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	33
utslib.citation.issue	11

Abstract:

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Semantic understanding is an essential research issue for many applications, such as social network analysis, collective intelligence and content computing, which tells the inner meaning of language form. Recently, Abstract Meaning Representation (AMR) is attracted by many researchers for its semantic representation ability on an entire sentence. However, due to the non-projectivity and reentrancy properties of AMR graphs, they lose some important semantic information in parsing from sentences. In this paper, we propose a general AMR parsing model which utilizes a two-stack-based transition algorithm for both Chinese and English datasets. It can incrementally parse sentences to AMR graphs in linear time. Experimental results demonstrate that it is superior in recovering reentrancy and handling arcs while is competitive with other transition-based neural network models on both English and Chinese datasets.

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http://hdl.handle.net/10453/149939