Cognitive memory-inspired sentence ordering model

Liu, W; Luo, X; Xuan, J; Xu, Z; Jiang, D

Cognitive memory-inspired sentence ordering model

Liu, W Luo, X Xuan, J

Xu, Z Jiang, D

Permalink

Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2016, 104 pp. 1 - 13
Issue Date:: 2016-07-15

Closed Access

	Filename	Description	Size
	1-s2.0-S0950705116300326-main.pdf	Published Version	1.4 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, W	en_US
dc.contributor.author	Luo, X	en_US
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908	en_US
dc.contributor.author	Xu, Z	en_US
dc.contributor.author	Jiang, D	en_US
dc.date.issued	2016-07-15	en_US
dc.identifier.citation	Knowledge-Based Systems, 2016, 104 pp. 1 - 13	en_US
dc.identifier.issn	0950-7051	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120520
dc.description.abstract	As the novel web social media emerges on the web, large scale unordered sentences are springing up in the forms: news headlines, microblogs, comments and so on. Although these massive sentences contain rich information, their loose semantic association and highly unordered semantic organization make web users extremely difficult to capture the rich information due to the lack of semantic coherence. Sentence ordering is a significant research area focusing on obtaining coherent sentence orders which could assist web user to easily understand these unordered sentences. Although many state-of-the-art coherence-based sentence ordering approaches have been proposed, some challenging issues remain unsolved: 1) what is knowledge foundation to support semantic representation and inference in sentence ordering; 2) what are the cognitive mechanisms used to guide sentence ordering; 3) how to collaborate different cognitive mechanisms for generating a coherent sentence order. To solve these issues, we propose a cognitive memory-inspired sentence ordering model. We propose three cognitive logical structures and construct their corresponding markov random fields to support semantic representation and inference. Besides, we propose three memory-inspired modules and their corresponding learning methods. These modules collaborate with each other to generate a coherent sentence order. Comparative experiments are conducted to evaluate the effectiveness of the proposed model. The results show our model can obtain coherent sentence orders with higher accuracy compared with the baseline methods.	en_US
dc.relation.ispartof	Knowledge-Based Systems	en_US
dc.relation.isbasedon	10.1016/j.knosys.2016.03.031	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Cognitive memory-inspired sentence ordering model	en_US
dc.type	Journal Article
utslib.citation.volume	104	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	104	en_US

Abstract:

As the novel web social media emerges on the web, large scale unordered sentences are springing up in the forms: news headlines, microblogs, comments and so on. Although these massive sentences contain rich information, their loose semantic association and highly unordered semantic organization make web users extremely difficult to capture the rich information due to the lack of semantic coherence. Sentence ordering is a significant research area focusing on obtaining coherent sentence orders which could assist web user to easily understand these unordered sentences. Although many state-of-the-art coherence-based sentence ordering approaches have been proposed, some challenging issues remain unsolved: 1) what is knowledge foundation to support semantic representation and inference in sentence ordering; 2) what are the cognitive mechanisms used to guide sentence ordering; 3) how to collaborate different cognitive mechanisms for generating a coherent sentence order. To solve these issues, we propose a cognitive memory-inspired sentence ordering model. We propose three cognitive logical structures and construct their corresponding markov random fields to support semantic representation and inference. Besides, we propose three memory-inspired modules and their corresponding learning methods. These modules collaborate with each other to generate a coherent sentence order. Comparative experiments are conducted to evaluate the effectiveness of the proposed model. The results show our model can obtain coherent sentence orders with higher accuracy compared with the baseline methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/120520