The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Probabilistic Topic Models
Abstract
Many chapters in this book illustrate that applying a statistical method such as latent semantic analysis (LSA; Landauer & Dumais, 1997; Landauer, Foltz, & Laham, 1998) to large databases can yield insight into human cognition. The LSA approach makes three claims: that semantic information can be derived from a word-document co-occurrence matrix; that dimensionality reduction is an essential part of this derivation; and that words and documents can be represented as points in Euclidean space. This chapter pursues an approach that is consistent with the first two of these claims, but differs in the third, describing a class of statistical models in which the semantic properties of words and documents are expressed in terms of probabilistic topics.
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Cites
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,908
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Markov Chain Monte Carlo in Practice
(1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,757
Landauer, T. K., & Dumais, S. T. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.211 · Cited by 6,282
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Self-citationAn introduction to latent semantic analysis
Landauer, T. K., Foltz, P. W., & Laham, D. (1998) · Discourse Processes · doi:10.1080/01638539809545028 · Cited by 4,880
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Seung, D., & Lee, L.-W. (2001) · Cited by 4,753
Unsupervised Learning by Probabilistic Latent Semantic Analysis
Hofmann, T. (2001) · Machine Learning · doi:10.1023/a:1007617005950 · Cited by 2,456
Hierarchical Dirichlet Processes
Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000302 · Cited by 2,120
Probabilistic Latent Semantic Analysis
Hofmann, T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.6705 · Cited by 2,047
Cited by
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_907 · Cited by 3,260
(2009) · Cited by 1,900
TwitterRank: finding topic-sensitive influential twitterers
Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010) · Proceedings of the third ACM international conference on Web search and data mining · doi:10.1145/1718487.1718520 · Cited by 1,742
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E. P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073791 · Cited by 1,700
Metadata
- Authors
- Steyvers, M., & Griffiths, T.
- Venue
- Handbook of Latent Semantic Analysis
- Publisher
- Routledge
- Published
- 2015-02-17
- OpenAlex
- W2334889010
- Cited by
- 1,011
- References
- 23
- FWCI
- 201.46
- Type
- book chapter
- Language
- en
- Open access
- closed
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