Probabilistic Latent Semantic Indexing Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval

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{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.

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@inproceedings{Hofmann1999ProbabilisticLS, title={Probabilistic Latent Semantic Indexing Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval}, author={Thomas Hofmann}, year={1999} }