Probabilistic Latent Semantic Indexing

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Probabilistic Latent Semantic Indexing (1999)

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by Thomas Hofmann

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@MISC{Hofmann99probabilisticlatent,
    author = {Thomas Hofmann},
    title = {Probabilistic Latent Semantic Indexing},
    year = {1999}
}

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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 methodsaswell as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.

Keyphrases

probabilistic latent semantic indexing singular value decomposition factor analysis expectation maximization algorithm count data retrieval experiment different dimensionality probabilistic variant statistical latent class model domain-specific synonymy polysemous word direct term text document training corpus substantial performance gain novel approach latent semantic indexing test collection utilized model solid statistical foundation proper generative data model

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