Abstract
Scatter/Gather systems are increasingly becoming useful in browsing document corpora. Usability of the present-day systems are restricted to monolingual corpora, and their methods for clustering and labeling do not easily extend to the multilingual setting, especially in the absence of dictionaries/machine translation. In this paper, we study the cluster labeling problem for multilingual corpora in the absence of machine translation, but using comparable corpora. Using a variational approach, we show that multilingual topic models can effectively handle the cluster labeling problem, which in turn allows us to design a novel Scatter/Gather system ShoBha. Experimental results on three datasets, namely the Canadian Hansards corpus, the entire overlapping Wikipedia of English, Hindi and Bengali articles, and a trilingual news corpus containing 41,000 articles, confirm the utility of the proposed system.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. JMLR (2003)
Carmel, D., Roitman, H., Zwerdling, N.: Enhancing cluster labeling using wikipedia. In: SIGIR 2009 (2009)
Carpineto, C., Osiski, S., Romano, G., Weiss, D.: A survey of web clustering engines. ACM Comput. Surv. (July 2009)
Chen, H.-H., Kuo, J.-J., Su, T.-C.: Clustering and Visualization in a Multi-lingual Multi-document Summarization System. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 266–280. Springer, Heidelberg (2003)
Chin, O.S., Kulathuramaiyer, N., Yeo, A.W.: Automatic discovery of concepts from text. In: WI 2006 (2006)
Cohen, J.: Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin (October 1968)
Cutting, D.R., Karger, D.R., Pedersen, J.O., Tukey, J.W.: Scatter/gather: a cluster-based approach to browsing large document collections. In: SIGIR 1992 (1992)
Geraci, F., Pellegrini, M., Maggini, M., Sebastiani, F.: Cluster generation and labeling for web snippets: A fast, accurate hierarchical solution. Internet Math. (2007)
Glover, E., Pennock, D.M., Lawrence, S., Krovetz, R.: Inferring hierarchical descriptions. In: CIKM 2002 (2002)
Honarpisheh, M.A., Ghassem-Sani, G., Mirroshandel, G.: A multi-document multi-lingual automatic summarization system. In: IJCNLP 2009 (2009)
Ke, W., Sugimoto, C.R., Mostafa, J.: Dynamicity vs. e ectiveness: studying online clustering for scatter/gather. In: SIGIR 2009 (2009)
Kuo, J.-J., Chen, H.-H.: Multidocument summary generation: Using informative and event words. TALIP (February 2008)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
Mimno, D., Wallach, H.M., Naradowsky, J., Smith, D.A., McCallum, A.: Polylingual topic models. In: EMNLP 2009 (2009)
Ming, Z.-Y., Wang, K., Chua, T.-S.: Prototype hierarchy based clustering for the categorization and navigation of web collections. In: SIGIR 2010 (2010)
Osinski, S., Weiss, D.: A concept-driven algorithm for clustering search results. IEEE Intell. Sys. (May 2005)
Radev, D.R., Jing, H., Styś, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Proc. Manag (November 2004)
Toda, H., Kataoka, R.: A clustering method for news articles retrieval system. In: WWW 2005 (2005)
Treeratpituk, P., Callan, J.: Automatically labeling hierarchical clusters. Digital Government Research (2006)
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Tholpadi, G., Das, M.K., Bhattacharyya, C., Shevade, S. (2012). Cluster Labeling for Multilingual Scatter/Gather Using Comparable Corpora. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_33
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DOI: https://doi.org/10.1007/978-3-642-28997-2_33
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