Discovering the core semantics of event from social media

Liu, W; Luo, X; Gong, Z; Xuan, J; Kou, NM; Xu, Z

Discovering the core semantics of event from social media

Liu, W Luo, X Gong, Z Xuan, J

Kou, NM Xu, Z

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Publication Type:: Journal Article
Citation:: Future Generation Computer Systems, 2016, 64 pp. 175 - 185
Issue Date:: 2016-11-01

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Field	Value	Language
dc.contributor.author	Liu, W	en_US
dc.contributor.author	Luo, X	en_US
dc.contributor.author	Gong, Z	en_US
dc.contributor.author	Xuan, J https://orcid.org/0000-0002-8367-6908	en_US
dc.contributor.author	Kou, NM	en_US
dc.contributor.author	Xu, Z	en_US
dc.date.available	2020-05-25T19:20:16Z
dc.date.issued	2016-11-01	en_US
dc.identifier.citation	Future Generation Computer Systems, 2016, 64 pp. 175 - 185	en_US
dc.identifier.issn	0167-739X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/100187
dc.description.abstract	© 2015 Elsevier B.V. As social media is opening up such as Twitter and Sina Weibo,1 large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in extracting core semantics accurately and efficiently. The proposed method can be applied to short text automatic generation, event discovery and summarization for big data analysis.	en_US
dc.relation.ispartof	Future Generation Computer Systems	en_US
dc.relation.isbasedon	10.1016/j.future.2015.11.023	en_US
dc.subject.classification	Distributed Computing	en_US
dc.title	Discovering the core semantics of event from social media	en_US
dc.type	Journal Article
utslib.citation.volume	64	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0803 Computer Software	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	64	en_US

Abstract:

© 2015 Elsevier B.V. As social media is opening up such as Twitter and Sina Weibo,1 large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in extracting core semantics accurately and efficiently. The proposed method can be applied to short text automatic generation, event discovery and summarization for big data analysis.

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http://hdl.handle.net/10453/100187