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. More specifically, this paper applies an LDA-based document model to the task of labelling blog posts with Wikipedia entries. 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. In the evaluation of the paper, we also show that the LDA-based document retrieval scheme outperforms our previous approach.
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References
Tunkelang, D.: Faceted Search. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool Publishers (2009)
Yokomoto, D., Makita, K., Utsuro, T., Kawada, Y., Fukuhara, T.: Utilizing Wikipedia in categorizing topic related blogs into facets. In: Proc. 12th PACLING, #20 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Wei, X., Croft, W.B.: LDA-Based document models for ad-hoc retrieval. In: Proc. 29th SIGIR, pp. 178–185 (2006)
Macdonald, C., Ounis, I., Soboroff, I.: Overview of the TREC-2009 blog track. In: Proc. TREC 2009 (2009)
Fujimura, K., Toda, H., Inoue, T., Hiroshima, N., Kataoka, R., Sugizaki, M.: BLOGRANGER - a multi-faceted blog search engine. In: Proc. 3rd Ann. Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics (2006)
Li, C., Yan, N., Roy, S.B., Lisham, L., Das, G.: Facetedpedia: Dynamic generation of query-dependent faceted interfaces for Wikipedia. In: Proc. 19th WWW, pp. 651–660 (2010)
Harashima, J., Kurohashi, S.: Summarizing search results using PLSI. In: Proc. 2nd Workshop on NLPIX, pp. 12–20 (2010)
Toda, H., Kataoka, R., Oku, M.: Search result clustering using informatively named entities. International Journal of Human-Computer Interaction, 3–23 (2007)
de Winter, W., de Rijke, M.: Identifying facets in query-biased sets of blog posts. In: Proc. ICWSM, pp. 251–254 (2007)
Shibata, T., Bamba, Y., Shinzato, K., Kurohashi, S.: Web information organization using keyword distillation based clustering. In: Proc. WI-IAT, pp. 325–330 (2009)
Hu, J., Fang, L., Cao, Y., Zeng, H.J., Li, H., Yang, Q., Chen, Z.: Enhancing text clustering by leveraging Wikipedia semantics. In: Proc. 31st SIGIR, pp. 179–186 (2008)
Carmel, D., Roitman, H., Zwerdling, N.: Enhancing cluster labeling using Wikipedia. In: Proc. 32nd SIGIR, pp. 139–146 (2009)
Hoffman, T.: Probabilistic latent semantic indexing. In: Proc. 22nd SIGIR, pp. 50–57 (1999)
Liu, X., Croft, W.B.: Cluster-based retrieval using language models. In: Proc. 27th SIGIR, pp. 186–193 (2004)
Phan, X.H., Nguyen, C.T.: GibbsLDA++: A C/C++ implementation of latent Dirichlet allocation (LDA) (2007)
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Yokomoto, D. et al. (2012). LDA-Based Topic Modeling in Labeling Blog Posts with Wikipedia Entries. In: Wang, H., et al. Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29426-6_15
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DOI: https://doi.org/10.1007/978-3-642-29426-6_15
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