Learning author-topic models from text corpora

ACM Transactions on Information Systems

Volumn 28, Issue 1, 2010, Pages

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

^a IBM HAIFA RESEARCH LAB   (Israel)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Author models; Gibbs sampling; Perplexity; Topic models; Unsupervised learning

Indexed keywords

AUTHOR MODELS; CITESEER; DOCUMENT RETRIEVAL; EXTRACTING INFORMATION; GIBBS SAMPLING; MARKOV CHAIN MONTE CARLO ALGORITHMS; NEURAL INFORMATION PROCESSING SYSTEMS; PERPLEXITY; STOCHASTIC PROCESS; TEXT COLLECTION; TEXT CORPORA; TOPIC MODEL; TWO STAGE; UNUSUAL PAPERS;

ABSTRACTING; DATA PROCESSING; DIGITAL LIBRARIES; MARKOV PROCESSES; MODELS; STOCHASTIC MODELS; UNSUPERVISED LEARNING;

PROBABILITY DISTRIBUTIONS;

EID: 80051615661     PISSN: 10468188     EISSN: 15582868     Source Type: Journal    
DOI: 10.1145/1658377.1658381     Document Type: Article

References (53)

1. BERRY, M. W., DUMAIS, S. T., AND O'BRIEN, G. W. 1994. Using linear algebra for intelligent information retrieval. SIAM Rev. 573-595.
2. BLEI, D. AND LAFFERTY, J. 2006a. Correlated topic models. In Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf, and J. Platt Eds., MIT Press, Cambridge, MA, 147-154.
3. BLEI, D. AND LAFFERTY, J. 2006b. Correlated topic models. In Proceedings of the 23rd International Conference on Machine Learning. ACM Press, New York, NY, 113-120.
4. BLEI, D. M., NG, A. Y., AND JORDAN, M. I. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993-1022.
5. BOX, G. E. P. AND TIAO, G. C. 1973. Bayesian Inference in Statistical Analysis. Addison-Wesley, Reading, MA.
6. BROOKS, S. 1998. Markov chain Monte Carlo method and its application. Statistician 47, 69-100. (Pubitemid 128469062)
7. BUNTINE, W. L. AND JAKULIN, A. 2004. Applying discrete PCA in data analysis. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. M. Chickering and J. Halpern Eds. Morgan Kaufmann Publishers, San Francisco, CA, 59-66.
8. CANNY, J. 2004. GaP: a factor model for discrete data. In Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval (SIGIR). ACM Press, New York, NY, 122-129.
9. CHEMUDUGUNTA, C., SMYTH, P., AND STEYVERS, M. 2007. Modeling general and specific aspects of documents with a probabilistic topic model. In Advances in Neural Information Processing Systems 19, B. Schölkopf, J. Platt, and T. Hoffman Eds., MIT Press, Cambridge, MA, 241-248.
10. COHN, D. ANDHOFMANN, T. 2001. The missing link-a probabilistic model of document content and hypertext connectivity. In Advances in Neural Information Processing Systems 13, T. K. Leen, T. G. Dietterich, and V. Tresp Eds., MIT Press, Cambridge, MA, 430-436.
11. CUTTING, D. R., KARGER, D., PEDERSEN, J. O., AND TUKEY, J.W. 1992. Scatter/gather: A cluster-based approach to browsing large document collections. In Proceedings of the 15th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, 318-329. (Pubitemid 23630481)
12. DEERWESTER, S. C., DUMAIS, S. T., LANDAUER, T. K., FURNAS, G. W., AND HARSHMAN, R. A. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Inform. Sci. 41, 6, 391-407.
13. DHILLON, I. S. AND MODHA, D. S. 2001. Concept decompositions for large sparse text data using clustering. Mach. Learn. 42, 1/2, 143-175. (Pubitemid 32872402)
14. DIEDERICH, J., KINDERMANN, J., LEOPOLD, E., AND PAASS, G. 2003. Authorship attribution with support vector machines. Appl. Intell. 19, 1, 109-123.
15. EROSHEVA, E., FIENBERG, S., AND LAFFERTY, J. 2004. Mixed-membership models of scientific publications. Proc. Nat. Acad. Sci. 101, 5220-5227. (Pubitemid 38469130)
16. ERTEN, C., HARDING, P. J., KOBOUROV, S. G., WAMPLER, K., AND YEE, G. 2003. Exploring the computing literature using temporal graph visualization. Tech. rep., Department of Computer Science, University of Arizona.
17. GILKS, W., RICHARDSON, S., AND SPIEGELHALTER, D. 1996. Markov Chain Monte Carlo in Practice. Chapman & Hall, New York, NY.
18. GRAY, A., SALLIS, P., AND MACDONELL, S. 1997. Software forensics: Extending authorship analysis techniques to computer programs. In Proceedings of the 3rd Biannual Conference of the International Association of Forensic Linguists (IAFL). 1-8.
19. GRIFFITHS, T. L. AND STEYVERS, M. 2004. Finding scientific topics. Proc. Nat. Acad. Sci. 101, 5228-5235. (Pubitemid 38469131)
20. GRIFFITHS, T. L., STEYVERS, M., BLEI, D. M., AND TENENBAUM, J. B. 2005. Integrating topics and syntax. In Advances in Neural Information Processing Systems 17, L. K. Saul, Y. Weiss, and L. Bottou Eds., MIT Press, Cambridge, MA.
21. GRUBER, A., ROSEN-ZVI, M., AND WEISS, Y. 2007. Hidden topic Markov models. In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTATS).
22. HOFMANN, T. 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd Annual ACM Conference on Research and Development in Information Retrieval. ACM Press, New York, NY, 50-57.
23. HOLMES, D. I. 1998. The evolution of stylometry in humanities scholarship. Literary Ling. Comput. 13, 3, 111-117.
24. IYER, R. AND OSTENDORF, M. 1999. Modelling long distance dependence in language: Topic mixtures versus dynamic cache models. IEEE Trans. Speech Audio Process. 7, 1, 30-39.
25. KAUTZ, H., SELMAN, B., AND SHAH, M. 1997. Referral Web: combining social networks and collaborative filtering. Comm. ACM 40, 3, 63-65. (Pubitemid 127441132)
26. KJELL, B. 1994. Authorship determination using letter pair frequency features with neural network classifiers. Literary Ling. Comput. 9, 2, 119-124.
27. LAGUS, K., HONKELA, T., KASKI, S., AND KOHONEN, T. 1999. WEBSOM for textual data mining. Artif. Intell. Rev. 13, 5-6, 345-364. (Pubitemid 30594818)
28. LAWRENCE, S., GILES, C. L., AND BOLLACKER, K. 1999. Digital libraries and autonomous citation indexing. IEEE Comput. 32, 6, 67-71.
29. LEE, D. D. AND SEUNG, H. S. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788.
30. LI, W. AND MCCALLUM, A. 2006. DAG-structured mixture models of topic correlations. In Proceedings of the 23rd International Conference on Machine Learning. ACM Press, New York, NY, 577-584.
31. MCCAIN, K. W. 1990. Mapping authors in intellectual space: a technical overview. J. Amer. Soc.Inform. Sci. 41, 6, 433-443.
32. MCCALLUM, A. 1999. Multi-label text classification with amixture model trained by EM. In AAAI Workshop on Text Learning.
33. MCCALLUM, A., CORRADA-EMMANUEL, A., AND WANG, X. 2005. Topic and role discovery in social networks. In Proceedings of the 19th International Joint Conference on Artificial Intelligence, 786-791.
34. MCCALLUM, A., NIGAM, K., AND UNGAR, L. H. 2000. Efficient clustering of high-dimensional data sets with application to reference matching. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, NY, 169-178.
35. MEI, Q. AND ZHAI, C. 2006. Amixture model for contextual text mining. In Proceedings of the 12th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, NY, 649-655. (Pubitemid 44535569)
36. MINKA, T. AND LAFFERTY, J. 2002. Expectation-propagation for the generative aspect model. In Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers, San Francisco, CA, 352-359.
37. MOSTELLER, F. AND WALLACE, D. 1964. Inference and Disputed Authorship: The Federalist Papers. Addison-Wesley, Reading, MA.
38. MUTSCHKE, P. 2003. Mining networks and central entities in digital libraries: a graph theoretic approach applied to co-author networks. Advanced in Intelligent Data Analysis, V, Lecture Notes in Computer Science, vol. 2810, Springer Verlag, 155-166.
39. NEWMAN, M. 2001. Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64, 1, 016131.
40. PONTE, J. M. AND CROFT, W. B. 1998. A language modeling approach to information retrieval. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 275-281.
41. POPESCUL, A., UNGAR, L. H., FLAKE, G. W., LAWRENCE, S., AND GILES, C. L. 2000. Clustering and identifying temporal trends in document databases. In Proceedings of the IEEE Advances in Digital Libraries 2000. IEEE Computer Society, Los Alamitos, CA, 173-182.
42. PRITCHARD, J., STEPHENS, M., AND DONNELLY, P. 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945-959. (Pubitemid 30397141)
43. ROBERTS, G. O. AND SAHU, S. K. 1997. Updating schemes, correlation structure, blocking and parameterisation for the Gibbs sampler. J. Royal Statist. Soc. B, 59, 291-317. (Pubitemid 127686955)
44. ROBERTSON, S. E., WALKER, S., JONES, S., HANCOCK-BEAULIEU, M. M., AND GATFORD, M. 1995. Okapi at TREC-3. In Proceedings of TREC. 109-126.
45. ROSEN-ZVI, M., GRIFFITHS, T., STEYVERS, M., AND SMYTH, P. 2004. The author-topic model for authors and documents. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, M. Chickering and J. Halpern, Eds. Morgam Kaufmann, San Francisco, CA, 487-494.
46. SPARCK JONES, K., WALKER, S., AND ROBERTSON, S. E. 2000. A probabilistic model of information retrieval: development and comparative experiments. Inform. Proc. Manag. 36, 6, 779-808.
47. STEYVERS, M., SMYTH, P., ROSEN-ZVI, M., AND GRIFFITHS, T. 2004. Probabilistic author-topic models for information discovery. In Proceedings of the 10th ACMInternational Conference on Knowledge Discovery and Data Mining. ACM Press, New York, 306-315. (Pubitemid 40114940)
48. TEH, Y. W., JORDAN, M. I., BEAL, M. J., AND BLEI, D. M. 2005. Hierarchical Dirichlet processes. In Advances in Neural Information Processing Systems 17, L. K. Saul, Y.Weiss, and L. Bottou Eds., MIT Press, Cambridge, MA.
49. THISTED, R. AND EFRON, B. 1987. Did Shakespeare write a newly-discovered poem? Biometrika 74, 445-455.
50. UEDA, N. AND SAITO, K. 2003. Parametric mixture models for multi-labeled text. In Advances in Neural Information Processing Systems 15, S. Becker, S. Thrun, and K. Obermayer Eds., MIT Press, Cambridge, MA, 721-728.
51. WEI, X. AND CROFT, W. B. 2006. LDA-based document models for ad hoc retrieval. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, 178-185.
52. WHITE, S. AND SMYTH, P. 2003. Algorithms for estimating relative importance in networks. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, NY, 266-275.
53. YANG, Y. 1999. An evaluation of statistical approaches to text categorization. Inform. Retriev. 1, 1- 2, 69-90.