The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Probabilistic Latent Semantic Indexing
Abstract
Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Research works printed but not linked: 14
- 10.1002/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9 · DOI · Paper page
- Dumais , S. T. Latent semantic indexing (lsi): Trec-3 report . In Proceedings of the Text REtrieval Conference (TREC-3) ( 1995 ), D. Harman, Ed. , pp. 219 -- 230 . Dumais, S. T. Latent semantic indexing (lsi): Trec-3 report. In Proceedings of the Text REtrieval Conference (TREC-3) (1995), D. Harman, Ed., pp. 219--30.
- Gildea , D. , and Hofmann , T . Topic-based language models using em . In Proceedings of the 6th European Conference on Speech Communication and Technology(EUROSPEECH) ( 1999 ). Gildea, D., and Hofmann, T. Topic-based language models using em. In Proceedings of the 6th European Conference on Speech Communication and Technology(EUROSPEECH) (1999).
- 10.5555/1624312.1624317 · doi:10.5555/1624312.1624317
- Hofmann , T. Probabilistic latent semantic analysis . In Proceedings of the 15th Conference on Uncertainty in AI ( 1999 ). Hofmann, T. Probabilistic latent semantic analysis. In Proceedings of the 15th Conference on Uncertainty in AI (1999).
- Hofmann , T. , Puzicha , J. , and Jordan , M. I . Unsupervised learning from dyadic data . In Advances in Neural Information Processing Systems ( 1999 ), vol. 11 . Hofmann, T., Puzicha, J., and Jordan, M. I. Unsupervised learning from dyadic data. In Advances in Neural Information Processing Systems (1999), vol. 11.
- Linguistic Data Consortium . TDT pilot study corpus. Catalog no. LDC98T25 , 1998 . Linguistic Data Consortium. TDT pilot study corpus. Catalog no. LDC98T25, 1998.
- McLachlan , G. , and Basford , K. E . Mixture Models . Marcel Dekker , INC , New York Basel, 1988 . McLachlan, G., and Basford, K. E. Mixture Models. Marcel Dekker, INC, New York Basel, 1988.
- Murray , M. K. , and Rice , J. W . Differential geometry and statistics. No. 48 in Monographs on statistics and applied probability . Chapman & Hal , 1993 . Murray, M. K., and Rice, J. W. Differential geometry and statistics. No. 48 in Monographs on statistics and applied probability. Chapman & Hal, 1993.
- 10.3115/981574.981598 · DOI · Paper page
- 10.1016/0167-8655(90)90010-Y · DOI · Paper page
- Salton , G. , and McGill , M. J. Introduction to Modern Information Retrieval . McGraw-Hill , 1983 . Salton, G., and McGill, M. J. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.
- Saul , L. , and Pereira , F . Aggregate and mixed order Markov models for statistical language processing . In Proceedings of the 2nd International Conference on Empirical Methods in Natural Language Processing ( 1997 ). Saul, L., and Pereira, F. Aggregate and mixed order Markov models for statistical language processing. In Proceedings of the 2nd International Conference on Empirical Methods in Natural Language Processing (1997).
- 10.1145/290941.290991 · doi:10.1145/290941.290991
Cited by
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
ArnetMiner: extraction and mining of academic social networks
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1402008 · Cited by 2,231
Latent semantic models for collaborative filtering
Hofmann, T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963774 · Cited by 1,378
Self-citationConvex and Semi-Nonnegative Matrix Factorizations
Ding, C. H. Q., Li, T., & Jordan, M. I. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.277 · Cited by 1,317
Metadata
- Authors
- Hofmann, T.
- Venue
- ACM SIGIR Forum
- Published
- 2017-08-02
- Volume
- 51
- Issue
- 2
- Pages
- 211-218
- ISSN
- 0163-5840 (ISSN-L)
- OpenAlex
- W2107743791
- Cited by
- 3,988
- References
- 2
- FWCI
- 141.20
- Type
- journal article
- Language
- en
- Open access
- closed
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