Visually summarizing the evolution of documents under a social tag

KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

Volumn , Issue , 2010, Pages 85-94

  Gohr, André ^a   Spiliopoulou, Myra ^b   Hinneburg, Alexander ^c  

^a LEIBNIZ INSTITUTE OF PLANT BIOCHEMISTRY   (Germany)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

^c MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

Author keywords

Knowledge management; Social web; Tag semantics; Topic modeling; Visualization

Indexed keywords

FOLLOWING PROBLEM; PROBABILISTIC LATENT SEMANTIC ANALYSIS; SOCIAL WEBS; TAG SEMANTICS; THIRD PARTIES; TOPIC MODEL; TOPIC MODELING; VISUALIZATION TECHNIQUE;

HYDRAULICS; INFORMATION RETRIEVAL; KNOWLEDGE MANAGEMENT; SEMANTICS; VISUALIZATION;

SEMANTIC WEB;

EID: 78651439853     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. AlSumait, L., Barbara, D., and Domeniconi, C. (2008). On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In ICDM.
2. Blei, D. and Lafferty, J. (2006). Dynamic topic models. In ICML.
3. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet Allocation. J. Mach. Learn. Res., 3: 993-1022.
4. Boyd-Graber, J., Chang, J., Gerrish, S., Wang, C., and Blei, D. (2009). Reading tea leaves: How humans interpret topic models. In Neural Information Processing Systems (NIPS).
5. Chou, T.-C. and Chen, M. C. (2008). Using incremental PLSI for threshold-resilient online event analysis. IEEE Trans, on Knowl. and Data Eng., 20 (3):289-299.
6. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39 (1): 1-38.
7. Gohr, A., Hinneburg, A., Schult, R., and Spiliopoulou, M. (2009). Topic evolution in a stream of documents. In SIAM Data Mining Conf. (SDM;09), pages 378-385, Reno, CA.
8. Goyal, A., Bonchi, F., and Lakshmanan, L. V. (2008). Discovering leaders from community actions. In CIKM'08, pages 499-508, Napa Valley, CA, USA. ACM.
9. Guha, S., Meyerson, A., Mishra, N., Motwani, R., and O'Callaghan, L. (2003). Clustering data streams: Theory and practice. IEEE Trans. of Knowlende and Data Eng., 15 (3):515-528.
10. Havre, S., Hetzler, E., Whitney, P., and Nowell, L. (2002). Themeriver: Visualizing thematic changed in large document collections. IEEE Trans. Visualization and Computer Graphics, 8 (1): 9-20. (Pubitemid 34573479)
11. Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42 (1): 177-196. (Pubitemid 32872403)
12. Mei, Q., Shen, X., and Zhai, C. (2007). Automatic labeling of multinomial topic models. In KDD, pages 490-499. (Pubitemid 350229234)
13. Mei, Q. and Zhai, C. (2005). Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In SIGKDD, pages 198-207, New York, NY, USA. ACM. (Pubitemid 43218281)
14. Moxley, E., Kleban, J., Xu, J., and Manjunath, B. S. (2009). Not all tags are created equal: learning flickr tag semantics for global annotation. In ICME'09: Proceedings of the 2009 IEEE international conference on Multimedia and Expo, pages 1452-1455, Piscataway, NJ, USA. IEEE Press.
15. Suchanek, F. M., Vojnovic, M., and Gunawardena, D. (2008). Social tags: Meanings and suggestions. In CIKM'08, pages 223-232, Napa Valley, CA, USA. ACM.
16. W. S. Cleveland (1985, 1994). The Elements of Graphing Data. Hobart Press, Summit, New Jersey, U.S.A.
17. Wang, X. and McCallum, A. (2006). Topics over time: a non-markov continuous-time model of topical trends. In SIGKDD, pages 424-433. ACM. (Pubitemid 44535539)