Sequential Latent Dirichlet Allocation: Discover underlying topic structures within a document

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 148-157

  Du, Lan ^a,b   Buntine, Wray ^a,b   Jinr, Huidong ^a,b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b CSIRO   (Australia)

Author keywords

Collapsed Gibbs sampler; Document structure; Latent Dirichlet Allocation; Poisson Dirichlet process

Indexed keywords

DIRICHLET PROCESS; DOCUMENT STRUCTURE; GIBBS SAMPLERS; GIBBS SAMPLING; LATENT DIRICHLET ALLOCATION; LATENT DIRICHLET ALLOCATIONS; PATENT DOCUMENTS; RANDOM MIXTURES; SEQUENTIAL STRUCTURE; TOPIC STRUCTURES;

DATA MINING;

SAMPLING;

EID: 79951763363     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.51     Document Type: Conference Paper

References (20)

1. W. Buntine and A. Jakulin, "Discrete components analysis," in Subspace, Latent Structure and Feature Selection Techniques. Springer-Verlag, 2006.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent Dirichlet allocation," J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.
3. D. M. Blei and J. D. Lafferty, "Dynamic topic models," in Proc. of the 23rd ICML, 2006, pp. 113-120.
4. X. Wang and A. McCallum, "Topics over time: a non-Markov continuous-time model of topical trends," in Proc. of the 12th SIGKDD, 2006, pp. 424-433. (Pubitemid 44535539)
5. L. AlSumait, D. Barbará, and C. Domeniconi, "On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking," in Proc. of the 8th ICDM, 2008, pp. 3-12.
6. R. M. Nallapati, S. Ditmore, J. D. Lafferty, and K. Ung, "Multiscale topic tomography," in Proc. of the 13th SIGKDD, 2007, pp. 520-529. (Pubitemid 350229237)
7. T. L. Griffiths, M. Steyvers, D. M. Blei, and J. B. Tenenbaum, "Integrating topics and syntax," in NIPS 17, 2005, pp. 537-544.
8. M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth, "The author-topic model for authors and documents," in Proc. of the 20th UAI, 2004, pp. 487-494.
9. H. Wang, M. Huang, and X. Zhu, "A generative probabilistic model for multi-label classification," in Proc. of the 8th ICDM, 2008, pp. 628-637.
10. D. Blei and J. Lafferty, "Correlated topic models," in NIPS 18, 2006, pp. 147-154.
11. D. M. Blei, T. L. Griffiths, and M. I. Jordan, "The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies," J. ACM, vol. 57, no. 2, pp. 1-30, 2010.
12. M. M. Shafiei and E. E. Milios, "Latent Dirichlet co-clustering," in Proc. of the 6th ICDM, 2006, pp. 542-551. (Pubitemid 47485833)
13. J. Pitman and M. Yor, "The two-parameter Poisson-Diriclet distribution derived from a stable subordinator," Annals of Probability, vol. 25, no. 2, pp. 855-900, 1997.
14. H. Ishwaran and L. F. James, "Gibbs sampling methods for stick breaking priors," Journal of the American Statistical Association, pp. 161-173, March 2001.
15. W. Buntine and M. Hutter, "A Bayesian review of the Poisson-Dirichlet process," Submitted for publication, 2010.
16. Y. Teh, "A Bayesian interpretation of interpolated Kneser-Ney," School of Computing, National University of Singapore, Tech. Rep. TRA2/06, 2006.
17. Q. He, B. Chen, J. Pei, B. Qiu, P. Mitra, and L. Giles, "Detecting topic evolution in scientific literature: how can citations help?" in The 18th CIKM, 2009, pp. 957-966.
18. T. L. Griffiths and M. Steyvers, "Finding scientific topics." Natl Acad Sci USA, vol. 101 Suppl 1, pp. 5228-5235, 2004. (Pubitemid 38469131)
19. L. Du, W. Buntine, and H. Jin, "A segmented topic model based on the two-parameter Poisson-Dirichlet process," Mach. Learn., vol. 81, no. 1, pp. 5-19, 2010.
20. T. P. Minka, "Estimating a Dirichlet distribution," MIT, Tech. Rep., 2000.