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SIGIR
1999
ACM

Probabilistic Latent Semantic Indexing

9 years 6 months ago
Probabilistic Latent Semantic Indexing
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 todeal 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 methods as well as over LSI. In particular, the combination of models with di erent dimensionalities has proven to be advantageous.
Thomas Hofmann
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where SIGIR
Authors Thomas Hofmann
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