Topic identification in dynamical text by complexity pursuit

Neural Processing Letters

Volumn 17, Issue 1, 2003, Pages 69-83

  Bingham, Ella ^a   Kabán, Ata ^a,b   Girolami, Mark ^a,c  

^a AALTO UNIVERSITY   (Finland)

^b EÖTVÖS LORÁND UNIVERSITY   (Hungary)

^c UNIVERSITY OF THE WEST OF SCOTLAND   (United Kingdom)

Author keywords

Chat line discussion; Complexity pursuit; Dynamical text; Independent component analysis; Time series

Indexed keywords

BLIND SOURCE SEPARATION; COMPUTATIONAL COMPLEXITY; INFORMATION RETRIEVAL; INTERNET; STATISTICAL METHODS;

DYNAMICAL TEXT;

TEXT PROCESSING;

EID: 0037312422     PISSN: 13704621     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1022990829563     Document Type: Article

References (30)

1. Allan, J., Carbonell, J., Doddington, G., Yamron, J. and Yang, Y.: Topic detection and tracking pilot study. Final report, In: Proc. of DARPA Broadcast News Transcription and Understanding Workshop, 1998, pp. 194-218.
2. Baeza-Yates, R. A. and Ribeiro-Neto, B.: Modern Information Retrieval, New York: ACM Press, 1999.
3. Belouchrani, A., Meraim, K. A., Cardoso, J.-F. and Moulines, E.: A blind source separation technique based on second order statistics, IEEE Tr. on Signal Processing, 45(2) (1997), 434-444.
4. Berry, M. W., Dumais, S. T. and Letsche, T. A.: Computational methods for intelligent information access, In: Proc. of Supercomputing '95, San Diego, CA: USA, 1995.
5. Bingham, E., Kabán, A. and Girolami, M.: Finding topics in dynamical text: application to chat line discussions, In: 10th Int. World Wide Web Conf. Poster Proc., 2001, pp. 198-199.
6. Comon, P.: Independent component analysis - a new concept? Signal Processing, 36 (1994), 287-314.
7. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K. and Harshman, R.: Indexing by latent semantic analysis, Journal of the American Society for Information Science, 41(6) (1990), 391-407.
8. Friedman, J. H. and Tukey, J. W.: A projection pursuit algorithm for exploratory data analysis, IEEE Tr. of Computers, c-23(9) (1974), 881-890.
9. Hofmann, T.: Probabilistic Latent Semantic Analysis, In: Proc. 15th Annual Conf. on Uncertainty in Artificial Intelligence (UAI'99), Sweden: Stockholm, 1999.
10. Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis, IEEE Tr. on Neural Networks, 10(3) (1999), 626-634.
11. Hyvärinen, A.: Complexity pursuit: separating interesting components from time-series, Neural Computation, 13(4) (2001), 883-898.
12. Hyvärinen, A., Karhunen, J. and Oja, E.: Independent component analysis, Wiley Interscience, 2001.
13. Isbell, C. L. and Viola, P.: Restucturing sparse high dimensional data for effective retrieval, In: Advances in Neural Information Processing Systems 11, 1998, pp. 480-486.
14. Jutten, C. and Herault, J.: Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture, Signal Processing, 24 (1991), 1-10.
15. Kabán, A. and Girolami, M.: Unsupervised topic separation and keyword identification in document collections: a projection approach, Technical Report 10, Dept. of Computing and Information Systems, Univ. of Paisley, 2000.
16. Kabán, A. and Girolami, M.: A combined latent class and trait model for the analysis and visualization of discrete data, IEEE Tr. on Pattern Analysis. 23(8) (2001), 859-872.
17. Kabán, A. and Girolami, M.: A dynamic probabilistic model to visualize topic evolution in text streams, Journal of Intelligent Information Systems, Special Issue on Automated Text Categorization. 18(2) (2002).
18. Katz, S.: Distribution of content words and phrases in text and language modeling, Natural Language Engineering, 2(1) (1996), 15-59.
19. Kohonen, T., Kaski, S., Lagus, K., Salojärvi, J., Honkela, J., Paatero, V. and Saarela, A.: Self organization of a massive document collection, IEEE Tr. on Neural Networks, 11(3) (2000) 574-585. Special Issue on Neural Networks for Data Mining and Knowledge Discovery.
20. Kolenda, T. and Hansen, L. K.: Dynamical components of chat, Technical report Technical University of Denmark, 2000.
21. Kolenda, T., Hansen, L. K. and Larsen, J.: Signal detection using ICA: application to chat room topic spotting, In: Lee and Jung and Makeig and Sejnowski (eds.): Proc. of the Third International Conference on Independent Component Analysis and Signal Separation (ICA2001), San Diego, CA: USA pp. 540-545, 2001.
22. Kolenda, T., Hansen, L. K. and Sigurdsson, S.: Independent components in text, In: M. Girolami (ed.): Advances in Independent Component Analysis, Springer-Verlag, 2000, Chapt. 13, pp. 235-256.
23. Molgedey, L. and Schuster, H. G.: Separation of a mixture of independent signals using time delayed correlations, Phys. Review Letters, 72(23) (1994), 3634-3637.
24. TD: Combining higher-order statistics and temporal information for blind source separation (with noise), In: Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), France: Aussois. 1999, pp. 87-92.
25. Pajunen, P.: Blind source separation using algorithmic information theory, Neurocomputing, 22 (1998), 35-48.
26. Pajunen, P.: Blind source separation of natural signals based on approximate complexity minimization, In: Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), France: Aussois, 1999, pp. 267-270.
27. Papadimitriou, C., Raghavan, P., Tamaki, H. and Vempala, S.: Latent semantic indexing: a probabilistic analysis, In: Proc. 17th ACM Symp. Principles of Database Systems, Seattle, 1998, pp. 159-168.
28. Salton, G. and McGill, M.J.: Introduction to modern information retrieval, New York: McGraw-Hill, 1983.
29. Slaney, M. and Ponceleon, D.: Hierarchical segmentation: finding changes in a text signal. In: Proc. of the SIAM Text Mining 2001 Workshop, Chicago, IL: 2001, pp. 6-13.
30. Stone, J. V.: Blind source separation using temporal predictability, Neural Computation, 13(4) (2001).