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Probabilistic latent semantic indexing
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Source Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval table of contents
Berkeley, California, United States
Pages: 50 - 57  
Year of Publication: 1999
ISBN:1-58113-096-1
Author
Thomas Hofmann  International Computer Science Institute, Berkeley, CA & EECS Department, CS Division, UC Berkeley
Sponsor
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 82,   Downloads (12 Months): 642,   Citation Count: 205
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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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DEMPSTER, A., LAIRD, N., AND RUBIN, D. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. B 39 (1977), 1-38.
 
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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.
 
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GILDEA, D., AND HOFMANN, T. Topic-based language models using em. In Proceedings of the 6th European Conference on Speech Communication and Technology(EUROSPEECIt) (1999).
 
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HOFMANN, T. Probabilistic latent semantic analysis. In Proceedings of the 15th Conference on Uncertainty in AI (1999).
 
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LINGUISTIC DATA CONSORTIUM. TDT pilot study corpus. Catalog no. LDC98T25, 1998.
 
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MCLACHLAN, G., AND BASFORD, K. E. Mixture Models. Marcel Dekker, INC, New York Basel, 1988.
 
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MuaaaY, M. K., AND RICE, J. W. Differential geometry and statistics. No. 48 in Monographs on statistics and applied probability. Chapman & Hal, 1993.
 
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SAUL, g., AND PEREIRA, F. Aggregate and mixedorder Markov models for statistical language processing. In Proceedings of the 2nd International Conference on Empirical Methods in Natural Language Processing (1997).
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CITED BY  205