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Cited by 3,988

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Probabilistic Latent Semantic Indexing

Hofmann, Thomas·2017·ACM SIGIR Forum
DOI 10.1145/3130348.3130370
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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 methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.

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Research works printed but not linked: 14

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Metadata

Authors
Hofmann, T.
Venue
ACM SIGIR Forum
Publisher
Association for Computing Machinery (ACM)
Published
2017-08-02
Volume
51
Issue
2
Pages
211-218
ISSN
0163-5840 (ISSN-L)
DOI
10.1145/3130348.3130370
OpenAlex
W2107743791
Cited by
3,988
References
2
FWCI
141.20
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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