Abstract
We discuss the problem of discovering associations between typical situations (scenes) in our daily lives and their characteristic items, which refer to anything from real objects to imaginary beings or abstract concepts. Once scenes are associated with items, the scenes can be further computationally analyzed (e.g., compared, tracked) on the basis of their associated items. In our approach for mining such associations, a list L of items and a set D of Web documents, in which scenes are identified, are first prepared. Next, D is divided using latent Dirichlet allocation (LDA) into clusters, each of which can be regarded as corresponding to a distinct characteristic scene. Then, the relevance between the scenes and items in L is estimated on the basis of the statistical significance of occurrence of items in the clusters. We developed two simple techniques for improving the quality (consistency) of the clustering result obtained using LDA with the expectation that the improved clustering result yields better performance in revealing item-scene associations. The most effective of the two techniques, PACA, purifies original clusters (i.e., eliminates unwanted elements in each cluster) created using a clustering algorithm by using the outcome from another clustering algorithm. Through an experiment using pages in a social Q&A site, we verified the effectiveness of the cluster purification techniques and the total effectiveness of our approach of associating items with scenes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Swan, R., Allan, J.: Extracting significant time varying features from text. In: Proceedings of the 8th International Conference on Information and Knowledge Management, pp. 38–45 (1999)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Wang, X., Mccallum, A.: Topics over time: A non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 424–433 (2006)
Girju, R., Badulescu, A., Moldovan, D.: Automatic Discovery of Part-Whole Relations 32(1), 83–135 (2006)
Pantel, P., Pennacchiotti, M.: Espresso: leveraging generic patterns for automatically harvesting semantic relations. In: Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics, pp. 113–120 (2006)
De Saeger, S., Torisawa, K., Kazama, J.: Looking for trouble. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 185–192 (2008)
Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research 3, 583–617 (2003)
Ghaemi, R., Sulaiman, M.N., Ibrahim, H., Mustapha, N.: A Survey: Clustering Ensembles Techniques. World Academy of Science, Engineering and Technology 38, 644–653 (2009)
Zhao, Y., Karypis, G.: Criterion functions for document clustering: Experiments and analysis. Technical Report CS 01-040, Department of Computer Science, University of Minnesota (2001)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)
Salton, G.: Automatic Information Organization and Retrieval. McGraw-Hill (1968)
McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Newman, M.E.J.: Networks: An Introduction. Oxford University Press (2010)
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications 10(2), 191–218 (2006)
Orman, G.K., Labatut, V.: A Comparison of Community Detection Algorithms on Artificial Networks. In: Proceedings of the 12th International Conference on Discovery Science, pp. 242–256 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sato, Sy., Takahashi, M., Matsuo, M. (2013). Associating Items with Scenes Identified in Social Q&A Data. In: Haller, A., Huang, G., Huang, Z., Paik, Hy., Sheng, Q.Z. (eds) Web Information Systems Engineering – WISE 2011 and 2012 Workshops. WISE WISE 2011 2012. Lecture Notes in Computer Science, vol 7652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38333-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-38333-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38332-8
Online ISBN: 978-3-642-38333-5
eBook Packages: Computer ScienceComputer Science (R0)