Latent Semantic Indexing: A Probabilistic Analysis
نویسندگان
چکیده
منابع مشابه
A probabilistic model for Latent Semantic Indexing
Dimension reduction methods, such as Latent Semantic Indexing (LSI), when applied to semantic space built upon text collections, improve information retrieval, information filtering and word sense disambiguation. A new dual probability model based on the similarity concepts is introduced to provide deeper understanding of LSI. Semantic associations can be quantitatively characterized by their s...
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Probabilistic Latent Semantic Analysis (pLSA) is a technique from the category of topic models. Its main goal is to model cooccurrence information under a probabilistic framework in order to discover the underlying semantic structure of the data. It was developed in 1999 by Th. Hofmann [7] and it was initially used for text-based applications (such as indexing, retrieval, clustering); however i...
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Probabilistic Latent Semantic Indexing (PLSI) is a statistical technique for automatic document indexing. A novel method is proposed for updating PLSI when new documents arrive. The proposed method adds incrementally the words of any new document in the term-document matrix and derives the updating equations for the probability of terms given the class (i.e. latent) variables and the probabilit...
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ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 2000
ISSN: 0022-0000
DOI: 10.1006/jcss.2000.1711