A topic-oriented syntactic component extraction model for social media

Xu, Y; Luo, T; Xu, G; Pan, R

A topic-oriented syntactic component extraction model for social media

Xu, Y Luo, T Xu, G

Pan, R

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Electrical Engineering, 2012, 182 LNEE pp. 221 - 229
Issue Date:: 2012-12-13

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Field	Value	Language
dc.contributor.author	Xu, Y	en_US
dc.contributor.author	Luo, T	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Pan, R	en_US
dc.date.issued	2012-12-13	en_US
dc.identifier.citation	Lecture Notes in Electrical Engineering, 2012, 182 LNEE pp. 221 - 229	en_US
dc.identifier.isbn	9789400750852	en_US
dc.identifier.issn	1876-1100	en_US
dc.identifier.uri	http://hdl.handle.net/10453/107120
dc.description.abstract	Topic-oriented understanding is to extract information from various language instances, which reflects the characteristics or trends of semantic information related to the topic via statistical analysis. The syntax analysis and modeling is the basis of such work. Traditional syntactic formalization approaches widely used in natural language understanding could not be simply applied to the text modeling in the context of topic-oriented understanding. In this paper, we review the information extraction mode, and summarize its inherent relationship with the "Subject- Predicate" syntactic structure in Aryan language. And we propose a syntactic element extraction model based on the "topic-description" structure, which contains six kinds of core elements, satisfying the desired requirement for topic-oriented understanding. This paper also describes the model composition, the theoretical framework of understanding process, the extraction method of syntactic components, and the prototype system of generating syntax diagrams. The proposed model is evaluated on the Reuters 21578 and SocialCom2009 data sets, and the results show that the recall and precision of syntactic component extraction are up to 93.9% and 88%, respectively, which further justifies the feasibility of generating syntactic component through the word dependencies. © 2012 Springer Science+Business Media.	en_US
dc.relation.ispartof	Lecture Notes in Electrical Engineering	en_US
dc.relation.isbasedon	10.1007/978-94-007-5086-9_29	en_US
dc.title	A topic-oriented syntactic component extraction model for social media	en_US
dc.type	Conference Proceeding
utslib.citation.volume	182 LNEE	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0806 Information Systems	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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	182 LNEE	en_US

Abstract:

Topic-oriented understanding is to extract information from various language instances, which reflects the characteristics or trends of semantic information related to the topic via statistical analysis. The syntax analysis and modeling is the basis of such work. Traditional syntactic formalization approaches widely used in natural language understanding could not be simply applied to the text modeling in the context of topic-oriented understanding. In this paper, we review the information extraction mode, and summarize its inherent relationship with the "Subject- Predicate" syntactic structure in Aryan language. And we propose a syntactic element extraction model based on the "topic-description" structure, which contains six kinds of core elements, satisfying the desired requirement for topic-oriented understanding. This paper also describes the model composition, the theoretical framework of understanding process, the extraction method of syntactic components, and the prototype system of generating syntax diagrams. The proposed model is evaluated on the Reuters 21578 and SocialCom2009 data sets, and the results show that the recall and precision of syntactic component extraction are up to 93.9% and 88%, respectively, which further justifies the feasibility of generating syntactic component through the word dependencies. © 2012 Springer Science+Business Media.

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http://hdl.handle.net/10453/107120