Probabilistic latent semantic indexing
Bibliographic Data
| ID | 23329332 |
|---|---|
| Authors | Thomas Hofmann (0000-0002-2042-9192, International Computer Science Institute, corresponding author) |
| Year | 1999 |
| Pages | 50-57 |
| Publication date | 1999-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | CONFERENCE |
| Venue | Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (CONFERENCE) |
| Publisher | ACM (PUBLISHER) |
| DOI | 10.1145/312624.312649 |
| OpenAlex | W4233135949 |
| Language | EN |
| Citations received | 58 |
| References cited | 6 |
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 metho d s a s w ell as over LSI. In particular, the combination of models with di erent dimensionalities has proven to be advantageous.
Citation · Information retrieval · Latent semantic analysis · Library science · Natural language processing · Probabilistic latent semantic analysis · Probabilistic logic · Search engine indexing · World Wide Web · Artificial Intelligence · Computer Science · Data Management and Algorithms · Information Retrieval and Search Behavior · Topic Modeling
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| Unique citing works | 58 |
|---|---|
| Citations per year | 3,22 |
| Citation span | 2008 - 2026 (19) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 57 |