Latent semantic indexing: A probabilistic analysis

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Latent semantic indexing: A probabilistic analysis (1998)

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by Christos H. Papadimitriou , Prabhakar Raghavan , Hisao Tamaki , Santosh Vempala

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314 - 7 self

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@INPROCEEDINGS{Papadimitriou98latentsemantic,
    author = {Christos H. Papadimitriou and Prabhakar Raghavan and Hisao Tamaki and Santosh Vempala},
    title = {Latent semantic indexing: A probabilistic analysis},
    booktitle = {},
    year = {1998},
    pages = {159--168},
    publisher = {ACM press}
}

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Abstract

Latent semantic indexing (LSI) is an information retrieval technique based on the spectral analysis of the term-document matrix, whose empirical success had heretofore been without rigorous prediction and explanation. We prove that, under certain conditions, LSI does succeed in capturing the underlying semantics of the corpus and achieves improved retrieval performance. We also propose the technique of random projection as a way of speeding up LSI. We complement our theorems with encouraging experimental results. We also argue that our results may be viewed in a more general framework, as a theoretical basis for the use of spectral methods in a wider class of applications such as collaborative filtering.

Keyphrases

latent semantic indexing probabilistic analysis random projection rigorous prediction collaborative filtering general framework spectral method spectral analysis encouraging experimental result information retrieval technique empirical success certain condition improved retrieval performance term-document matrix theoretical basis wider class

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