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
Open accessAbstract
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{speci c 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 de nes a proper generative data model.Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching metho d s a s w ell as over LSI.In particular, the combination of models with dierent dimensionalities has proven to be advantageous.
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Cites
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: 11
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- DUMAIS S. T., 1995, Proceedings of the Text REtrieval Conference (TREC-3), 219
- GILDEA D., 1999, Proceedings of the 6th European Conference on Speech Communication and Technology(EUROSPEECIt)
- OFMANN W., 1999, Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI)
- HOFMANN T., 1999, Proceedings of the 15th Conference on Uncertainty in AI
- HOFMANN T., 1999, Advances in Neural Information Processing Systems, 11
- 10.3115/981574.981598 · DOI · Paper page
- 10.1016/0167-8655(90)90010-Y · DOI · Paper page
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- SAUL, 1997, Proceedings of the 2nd International Conference on Empirical Methods in Natural Language Processing
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Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
The probabilistic relevance framework: BM25 and beyond
Robertson, S., & Zaragoza, H. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000019 · Cited by 3,374
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
Machine learning and AI in marketing–Connecting computing power to human insights
Ma, L., & Sun, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.04.005 · Cited by 602
A survey on technical threat intelligence in the age of sophisticated cyber attacks
Tounsi, W., & Rais, H. (2018) · Computers & Security · doi:10.1016/j.cose.2017.09.001 · Cited by 474
Metadata
- Authors
- Hofmann, T.
- Venue
- Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 1999-08
- Pages
- 50-57
- OpenAlex
- W4233135949
- Cited by
- 3,976
- References
- 2
- FWCI
- 10.75
- Type
- conference paper
- Language
- en
- Open access
- bronze
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