An Analysis of Latent Semantic Term Self-Correlation
Author
Park, LAF; Ramamohanarao, KDate
2009-01-01Source Title
ACM Transactions on Information SystemsPublisher
ASSOC COMPUTING MACHINERYAffiliation
Computer Science And Software EngineeringMetadata
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Journal ArticleCitations
Park, L. A. F. & Ramamohanarao, K. (2009). An Analysis of Latent Semantic Term Self-Correlation. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 27 (2), https://doi.org/10.1145/1462198.1462200.Access Status
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C1 - Journal Articles Refereed
Abstract
<jats:p>Latent semantic analysis (LSA) is a generalized vector space method that uses dimension reduction to generate term correlations for use during the information retrieval process. We hypothesized that even though the dimension reduction establishes correlations between terms, the dimension reduction is causing a degradation in the correlation of a term to itself (self-correlation). In this article, we have proven that there is a direct relationship to the size of the LSA dimension reduction and the LSA self-correlation. We have also shown that by altering the LSA term self-correlations we gain a substantial increase in precision, while also reducing the computation required during the information retrieval process.</jats:p>
Keywords
Information Retrieval and Web Search; Electronic Information Storage and Retrieval ServicesExport Reference in RIS Format
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