News topic detection based on hierarchical clustering and named entity

Huang, S; Peng, X; Niu, Z; Wang, K

News topic detection based on hierarchical clustering and named entity

Huang, S Peng, X

Niu, Z Wang, K

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Publication Type:: Conference Proceeding
Citation:: NLP-KE 2011 - Proceedings of the 7th International Conference on Natural Language Processing and Knowledge Engineering, 2011, pp. 280 - 284
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Huang, S	en_US
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472	en_US
dc.contributor.author	Niu, Z	en_US
dc.contributor.author	Wang, K	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	NLP-KE 2011 - Proceedings of the 7th International Conference on Natural Language Processing and Knowledge Engineering, 2011, pp. 280 - 284	en_US
dc.identifier.isbn	9781612847283	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120549
dc.description.abstract	News topic detection is the process of organizing news story collections and real-time news/broadcast streams into news topics. While unlike the traditional text analysis, it is a process of incremental clustering, and generally divided into retrospective topic detection and online topic detection. This paper considers the feature changes of modern news data experienced from the past, and presents a new topic detection strategy based on hierarchical clustering and named entities. Topic detection process is also divided into retrospective and online steps, and named entities in the news stories are employed in the topic clustering algorithm. For the online step's efficiency and precision, this paper first clusters news stories in each time window into micro-clusters, and then extracts three representation vectors for each micro-cluster to calculate the similarity to existing topics. The experimental results show remarkable improvement compared with recently most applied topic detection method. © 2011 IEEE.	en_US
dc.relation.ispartof	NLP-KE 2011 - Proceedings of the 7th International Conference on Natural Language Processing and Knowledge Engineering	en_US
dc.relation.isbasedon	10.1109/NLPKE.2011.6138209	en_US
dc.title	News topic detection based on hierarchical clustering and named entity	en_US
dc.type	Conference Proceeding
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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

News topic detection is the process of organizing news story collections and real-time news/broadcast streams into news topics. While unlike the traditional text analysis, it is a process of incremental clustering, and generally divided into retrospective topic detection and online topic detection. This paper considers the feature changes of modern news data experienced from the past, and presents a new topic detection strategy based on hierarchical clustering and named entities. Topic detection process is also divided into retrospective and online steps, and named entities in the news stories are employed in the topic clustering algorithm. For the online step's efficiency and precision, this paper first clusters news stories in each time window into micro-clusters, and then extracts three representation vectors for each micro-cluster to calculate the similarity to existing topics. The experimental results show remarkable improvement compared with recently most applied topic detection method. © 2011 IEEE.

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http://hdl.handle.net/10453/120549