Probabilistic author-topic models for information discovery

KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2004, Pages 306-315

  Steyvers, Mark ^a   Smyth, Padhraic ^b   Rosen Zvi, Michal ^b   Griffiths, Thomas ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Irvine   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

Gibbs sampling; Text modeling; Unsupervised learning

Indexed keywords

ABSTRACTING; DATA MINING; DIGITAL LIBRARIES; INDEXING (OF INFORMATION); LEARNING ALGORITHMS; MARKOV PROCESSES; MONTE CARLO METHODS; PROBABILITY DISTRIBUTIONS; QUERY LANGUAGES; SELF ORGANIZING MAPS; SEMANTICS; TEXT PROCESSING;

GIBBS SAMPLING; ONLINE QUERY INTERFACE; TEXT MODELING; UNSUPERVISED LEARNING;

INFORMATION RETRIEVAL;

EID: 12244288622     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1014052.1014087     Document Type: Conference Paper

References (21)

1. Blei, D. M., Ng, A. Y., and Jordan, M. I., (2003) Latent Dirichlet allocation, Journal of Machine Learning Research 3, pp. 993-1022.
2. Buntine, W.L. (1994) Operations for learning with graphical models, Journal of Artificial Intelligence Research 2, pp. 159-225.
3. Cutting, D., Karger, D. R., Pederson, J., and Tukey, J. W. (1992) Scatter/Gather: a cluster-based approach to browsing large document collections, in Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 318-329.
4. Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A. (1990) Indexing by latent semantic analysis, Journal of the American Society of Information Science, 41(6), pp. 391-407.
5. Diederich, J., Kindermann, J., Leopold, E., and Paass, G. (2003) Authorship attribution with support vector machines, Applied Intelligence 19 (1).
6. Erten, C., Harding, P. J., Kobourov, S. G., Wampler, K., and Yee, G. (2003) Exploring the computing literature using temporal graph visualization, Technical Report, Department of Computer Science, University of Arizona.
7. Gray, A., Sallis, P., MacDonell, S. (1997) Software forensics: Extending authorship analysis techniques to computer programs, Proceedings of the 3rd Biannual Conference of the International Association of Forensic Linguists (IAFL), Durham NC.
8. Griffiths, T. L., and Steyvers, M. (2004) Finding scientific topics, Proceedings of the National Academy of Sciences, 101 (suppl. 1), 5228-5235.
9. Hofmann, T. (1999) Probabilistic latent semantic indexing, in Proceedings of the 22nd International Conference on Research and Development in Information Retrieval (SIGIR'99).
10. Kautz, H., Selman, B., and Shah, M. (1997) Referral Web: Combining social networks and collaborative filtering, Communications of the ACM, 3, pp. 63-65.
11. Lagus, K, Honkela, T., Kaski, S., and Kohonen, T. (1999) WEBSOM for textual data mining, Artificial Intelligence Review, 13 (5-6), pp. 345-364.
12. Lawrence, S., Giles, C. L., and Bollacker, K. (1999) Digital libraries and autonomous citation indexing, IEEE Computer, 32(6), pp. 67-71.
13. McCallum, A., Nigam, K., and Ungar, L. (2000) Efficient clustering of high-dimensional data sets with application to reference matching, in Proceedings of the Sixth ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 169-178.
14. Mosteller, F., and Wallace, D. (1964) Applied Bayesian and Classical Inference: The Case of the Federalist Papers, Springer-Verlag.
15. Mutschke, P. (2003) Mining networks and central entities in digital libraries: a graph theoretic approach applied to co-author networks, Intelligent Data Analysis 2003, Lecture Notes in Computer Science 2810, Springer Verlag, pp. 155-166
16. Newman, M. E. J. (2001) Scientific collaboration networks: I. Network construction and fundamental results, Physical Review E, 64, 016131.
17. Popescul, A., Flake, G. W., Lawrence, S., Ungar, L. H., and Giles, C. L. (2000) Clustering and identifying temporal trends in document databases, IEEE Advances in Digital Libraries, ADL 2000, pp. 173-182.
18. Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P. (2004) The author-topic model for authors and documents, Proceedings of the 20th U AI Conference, July 2004.
19. Thisted, B., and Efron, R. (1987) Did Shakespeare write a newly discovered poem?, Biometrika, pp. 445-455.
20. White, S. and Smyth, P. (2003) Algorithms for estimating relative importance in networks, in Proceedings of the Ninth ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 266-275.
21. Yang, Y. (1999) An evaluation of statistical approaches to text categorization, Information Retrieval, 1, pp. 69-90.