Presentation 2014/7/25
Finding Co-occurring Topics in Wikipedia Article Segments
Renzhi Wang, Jianmin Wu, Mizuho Iwaihara,
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Abstract(in English) Wikipedia is the largest online encyclopedia, in which articles form knowledgeable and semantic resources. A number of researches about detecting topics and semantic similarity analysis are based on the Wikipedia corpus. Identical topics in different articles indicate that the articles are related to each other about topics. Finding such co-occurring topics is useful to improve the accuracy of querying and clustering, and also to contrast related articles. Existing topic alignment work and topic relevance detection are based on term occurrence. In our research, we discuss incorporating latent topics existing in article segments by utilizing Latent Dirichlet Allocation (LDA), to detect topic relevance. We also study how segment proximities, arising from segment ordering and hyperlinks, shall be incorporated into topic detection and alignment.
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Keyword(in English) LDA / MLE / Link / Wikipedia
Paper # Vol.2014-DBS-159 No.8,Vol.2014-IFAT-115 No.8
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Committee DE
Conference Date 2014/7/25(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Finding Co-occurring Topics in Wikipedia Article Segments
Sub Title (in English)
Keyword(1) LDA
Keyword(2) MLE
Keyword(3) Link
Keyword(4) Wikipedia
1st Author's Name Renzhi Wang
1st Author's Affiliation Graduate School of Information, Production and Systems Waseda University()
2nd Author's Name Jianmin Wu
2nd Author's Affiliation Graduate School of Information, Production and Systems Waseda University
3rd Author's Name Mizuho Iwaihara
3rd Author's Affiliation Graduate School of Information, Production and Systems Waseda University
Date 2014/7/25
Paper # Vol.2014-DBS-159 No.8,Vol.2014-IFAT-115 No.8
Volume (vol) vol.114
Number (no) 173
Page pp.pp.-
#Pages 6
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