Association link network based semantic coherence measurement for short texts of web events

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

Association link network based semantic coherence measurement for short texts of web events

Liu, W Luo, X Xuan, J

Jiang, D Xu, Z

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Publisher:: Rinton Press
Publication Type:: Journal Article
Citation:: Journal of Web Engineering, 2017, 16, (1-2), pp. 39-62
Issue Date:: 2017-03-01

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Field	Value	Language
dc.contributor.author	Liu, W
dc.contributor.author	Luo, X
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908
dc.contributor.author	Jiang, D
dc.contributor.author	Xu, Z
dc.date.accessioned	2022-08-11T05:07:47Z
dc.date.available	2022-08-11T05:07:47Z
dc.date.issued	2017-03-01
dc.identifier.citation	Journal of Web Engineering, 2017, 16, (1-2), pp. 39-62
dc.identifier.issn	1540-9589
dc.identifier.issn	1544-5976
dc.identifier.uri	http://hdl.handle.net/10453/159932
dc.description.abstract	As novel web social Media emerges on the web, large-scale short texts are springing up. Although these massive short texts contain rich information, their disorder nature makes users difficult to obtain the desired knowledge from them, especially the semantic coherent knowledge. Different orders of these short texts often express different semantic coherence states. Therefore, how to automatically measure semantic coherence of short texts is a fundamental and significant problem for web knowledge services. Existing related works on the semantic coherence measurement of different orders of short texts/sentences seldom focus on graph structure of semantic link network for reecting coherence change, measuring coherence by these graph-based features and discovering some interesting coherence patterns. In this paper, we propose an association link network based semantic coherence measurement for short texts of web events. Our method firstly construct an association link network from which some graph-based features are then extracted to measure semantic coherence of different orders and lastly some coherence patterns are discovered for guiding automatically text ordering/generation. To validate correctness of our method, we conduct a series of experiments including sentence order permutation, sentence removal and adding/replacing sentence and compare with other two methods. The results show that our method can measure semantic coherence with higher accuracy and outperforms other methods in some experiments. Such method can be widely applied in web text automatic generation, web short text organization and web event summarization etc.
dc.language	English
dc.publisher	Rinton Press
dc.relation.ispartof	Journal of Web Engineering
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.title	Association link network based semantic coherence measurement for short texts of web events
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	08 Information and Computing 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 - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-11T05:07:46Z
pubs.issue	1-2
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	1-2

Abstract:

As novel web social Media emerges on the web, large-scale short texts are springing up. Although these massive short texts contain rich information, their disorder nature makes users difficult to obtain the desired knowledge from them, especially the semantic coherent knowledge. Different orders of these short texts often express different semantic coherence states. Therefore, how to automatically measure semantic coherence of short texts is a fundamental and significant problem for web knowledge services. Existing related works on the semantic coherence measurement of different orders of short texts/sentences seldom focus on graph structure of semantic link network for reecting coherence change, measuring coherence by these graph-based features and discovering some interesting coherence patterns. In this paper, we propose an association link network based semantic coherence measurement for short texts of web events. Our method firstly construct an association link network from which some graph-based features are then extracted to measure semantic coherence of different orders and lastly some coherence patterns are discovered for guiding automatically text ordering/generation. To validate correctness of our method, we conduct a series of experiments including sentence order permutation, sentence removal and adding/replacing sentence and compare with other two methods. The results show that our method can measure semantic coherence with higher accuracy and outperforms other methods in some experiments. Such method can be widely applied in web text automatic generation, web short text organization and web event summarization etc.

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http://hdl.handle.net/10453/159932