Indexing by latent semantic analysis

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Indexing by latent semantic analysis (1990)

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by Scott Deerwester , Susan T. Dumais , George W. Furnas , Thomas K. Landauer , Richard Harshman

Venue:	JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE
Citations:	3774 - 35 self

BibTeX

@ARTICLE{Deerwester90indexingby,
    author = {Scott Deerwester and Susan T. Dumais and George W. Furnas and Thomas K. Landauer and Richard Harshman},
    title = {Indexing by latent semantic analysis},
    journal = {JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE},
    year = {1990},
    volume = {41},
    number = {6},
    pages = {391--407}
}

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Abstract

A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 or-thogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are re-turned. initial tests find this completely automatic method for retrieval to be promising.

Keyphrases

latent semantic analysis weighted combination original matrix initial test automatic indexing new method automatic method supra-threshold cosine value linear combination factor weight or-thogonal factor implicit higher-order structure pseudo-document vector document matrix particular technique semantic structure relevant document large term singular-value decomposition item vector

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