ACM SIGIR Forum· 2017
Probabilistic Latent Semantic Indexing
- 4,064citations
- 2017year
Short summary
Probabilistic Latent Semantic Indexing (PLSI) introduces a novel statistical approach to document indexing that handles domain-specific synonymy and polysemous words, outperforming standard Latent Semantic Indexing (LSI).
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Key points
- Introduces Probabilistic Latent Semantic Indexing (PLSI) as a novel document indexing method.
- PLSI utilizes a statistical latent class model, offering a solid foundation unlike standard LSI.
- The model effectively handles domain-specific synonymy and polysemous words.
- Retrieval experiments show substantial performance gains over direct term matching and standard LSI.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. ACM SIGIR Forum, 2017 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science