Abstract
With topic modeling, scientists can explore and understand huge collections of unlabeled information.
- Blei, D. and Lafferty, J. Dynamic topic models. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, June 25-29, 2006. Google ScholarDigital Library
- Blei, D. and Lafferty, J. Topic models, Text Mining: Classification, Clustering, and Applications, (Srivastava, A. and Sahami, M., Eds), Taylor & Francis, London, England, 2009.Google Scholar
- Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., and Blei, D. Reading tea leaves: How humans interpret topic models. Twenty-Third Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, Dec. 7-12, 2009.Google Scholar
- 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.Google ScholarCross Ref
- Newman, D., Chemudugunta, C., Smyth, P., and Steyvers, M. Statistical entity-topic models. The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia, PA, August 23-26, 2006. Google ScholarDigital Library
Index Terms
- Topic models vs. unstructured data
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