@techreport{oai:ipsj.ixsq.nii.ac.jp:00102426,
 author = {Renzhi, Wang and Jianmin, Wu and Mizuho, Iwaihara and Renzhi, Wang and Jianmin, Wu and Mizuho, Iwaihara},
 issue = {8},
 month = {Jul},
 note = {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., 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.},
 title = {Finding Co-occurring Topics in Wikipedia Article Segments},
 year = {2014}
}