1999 ProbabilisticLatentSemanticIndexing

• (Hofmann, 1999a) ⇒ Thomas Hofmann. (1999). “Probabilistic Latent Semantic Indexing.” In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1999) doi:10.1145/312624.312649

Subject Headings: Probabilistic LSI Model, Probabilistic Generative Model, Latent Semantic Analysis, Topic Modeling.

Cited By

• ~1,587 http://scholar.google.com/scholar?q=%22Probabilistic+Latent+Semantic+Indexing%22+1999

2003

• (Blei, Ng & Jordan, 2003) ⇒ David M. Blei, Andrew Y. Ng , and Michael I. Jordan. (2003). “Latent Dirichlet Allocation.” In: The Journal of Machine Learning Research, 3.
• (Hotho et al., 2003) ⇒ Andreas Hotho, Steffen Staab, and Gerd Stumme. (2003). “Wordnet Improves Text Document Clustering.” In: Proceedings of the SIGIR Workshop on Semantic Web Workshop.

Quotes

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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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 ProbabilisticLatentSemanticIndexing	Thomas Hofmann			Probabilistic Latent Semantic Indexing			http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR99.pdf	10.1145/312624.312649