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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
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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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