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Probabilistic Latent Semantic Indexing
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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Cited by
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
ArnetMiner: extraction and mining of academic social networks
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Latent semantic models for collaborative filtering
Hofmann, T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963774 · Cited by 1,378
Self-citationConvex and Semi-Nonnegative Matrix Factorizations
Ding, C. H. Q., Li, T., & Jordan, M. I. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.277 · Cited by 1,317
Metadata
- Authors
- Hofmann, T.
- Venue
- ACM SIGIR Forum
- Published
- 2017-08-02
- Volume
- 51
- Issue
- 2
- Pages
- 211-218
- ISSN
- 0163-5840 (ISSN-L)
- OpenAlex
- W2107743791
- Cited by
- 3,988
- References
- 2
- FWCI
- 141.20
- Type
- journal article
- Language
- en
- Open access
- closed
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