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Labeling Blog Posts with Wikipedia Entries through LDA-Based Topic Modeling of Wikipedia

Kensaku Makita,
Hiroko Suzuki,
Daichi Koike,
Takehito Utsuro,
Yasuhide Kawada,
Tomohiro Fukuhara,

Abstract


Given a search query, most existing search engines simply return a ranked list of search results. However, it is often the case that those search result documents consist of a mixture of documents that are closely related to various contents. In order to address the issue of quickly overviewing the distribution of contents, this paper proposes a framework of labeling blog posts with Wikipedia entries through LDA (latent Dirichlet allocation) based topic modeling of Wikipedia. One of the most important advantages of this LDA-based document model is that the collected Wikipedia entries and their LDA parameters heavily depend on the distribution of keywords across all the search result of blog posts. This tendency actually contributes to quickly overviewing the search result of blog posts through the LDA-based topic distribution. We show that the LDA-based document retrieval scheme outperforms our previous approach. Finally, we compare the proposed approach to the standard LDA-based topic modeling without Wikipedia knowledge source. Both LDA-based topic modeling results have quite different nature and contribute to quickly overviewing the search result of blog posts in a quite complementary fashion.

Keywords


Blog; Wikipedia; Topic model; LDA; Topic analysis

Citation Format:
Kensaku Makita, Hiroko Suzuki, Daichi Koike, Takehito Utsuro, Yasuhide Kawada, Tomohiro Fukuhara, "Labeling Blog Posts with Wikipedia Entries through LDA-Based Topic Modeling of Wikipedia," Journal of Internet Technology, vol. 14, no. 2 , pp. 297-306, Mar. 2013.

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Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
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