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Probabilistic Latent Semantic Analysis (1999)

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by Thomas Hofmann
Venue:In Proc. of Uncertainty in Artificial Intelligence, UAI’99
Citations:771 - 9 self
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BibTeX

@INPROCEEDINGS{Hofmann99probabilisticlatent,
    author = {Thomas Hofmann},
    title = {Probabilistic Latent Semantic Analysis},
    booktitle = {In Proc. of Uncertainty in Artificial Intelligence, UAI’99},
    year = {1999},
    pages = {289--296}
}

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Abstract

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.

Keyphrases

probabilistic latent semantic analysis    latent semantic analysis    singular value decomposition    latent class model    related area    novel statistical technique    approach yield    co-occurrence data    natural language processing    information retrieval    applicable generalization    tempered em    consistent improvement    machine learning    principled approach    mixture decomposition    co-occurrence table    solid foundation    linear algebra    maximum likelihood model fitting   

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