Document (#28459)

Author

Lochbaum, K.E.
Streeter, A.R.

Title

Comparing and combining the effectiveness of latent semantic indexing and the ordinary vector space model for information retrieval

Source

Information processing and management. 25(1989) no.6, S. 665-676

Year

1989

Abstract

A retrievalsystem was built to find individuals with appropriate expertise within a large research establishment on the basis of their authored documents. The expert-locating system uses a new method for automatic indexing and retrieval based on singular value decomposition, a matrix decomposition technique related to the factor analysis. Organizational groups, represented by the documents they write, and the terms contained in these documents, are fit simultaneously into a 100-dimensional "semantic" space. User queries are positioned in the semantic space, and the most similar groups are returned to the user. Here we compared the standard vector-space model with this new technique and found that combining the two methods improved performance over either alone. We also examined the effects of various experimental variables on the system`s retrieval accuracy. In particular, the effects of: term weighting functions in the semantic space construction and in query construction, suffix stripping, and using lexical units larger than a a single word were studied.

Theme

Automatisches Indexieren
Retrievalstudien

Object

Latent Semantic Indexing

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4. Huang, Y.-L.: ¬A theoretic and empirical research of cluster indexing for Mandarine Chinese full text document (1998) 0.32

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5. Efron, M.: Query expansion and dimensionality reduction : Notions of optimality in Rocchio relevance feedback and latent semantic indexing (2008) 0.30

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