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Journal of computing and information technology, Vol. 17 No. 3, 2009.

Izvorni znanstveni članak
https://doi.org/10.2498/cit.1001268

On the Performance of Latent Semantic Indexing-based Information Retrieval

Cherukuri Aswani Kumar ; Intelligent Systems Division, School of Computing Sciences, VIT University, Vellore, India
Suripeddi Srinivas ; Fluid Dynamics Division, School of Science, VIT University, Vellore, India

Puni tekst: engleski, pdf (583 KB) str. 259-264 preuzimanja: 1.166* citiraj
APA 6th Edition
Aswani Kumar, C. i Srinivas, S. (2009). On the Performance of Latent Semantic Indexing-based Information Retrieval. Journal of computing and information technology, 17 (3), 259-264. https://doi.org/10.2498/cit.1001268
MLA 8th Edition
Aswani Kumar, Cherukuri i Suripeddi Srinivas. "On the Performance of Latent Semantic Indexing-based Information Retrieval." Journal of computing and information technology, vol. 17, br. 3, 2009, str. 259-264. https://doi.org/10.2498/cit.1001268. Citirano 24.02.2021.
Chicago 17th Edition
Aswani Kumar, Cherukuri i Suripeddi Srinivas. "On the Performance of Latent Semantic Indexing-based Information Retrieval." Journal of computing and information technology 17, br. 3 (2009): 259-264. https://doi.org/10.2498/cit.1001268
Harvard
Aswani Kumar, C., i Srinivas, S. (2009). 'On the Performance of Latent Semantic Indexing-based Information Retrieval', Journal of computing and information technology, 17(3), str. 259-264. https://doi.org/10.2498/cit.1001268
Vancouver
Aswani Kumar C, Srinivas S. On the Performance of Latent Semantic Indexing-based Information Retrieval. Journal of computing and information technology [Internet]. 2009 [pristupljeno 24.02.2021.];17(3):259-264. https://doi.org/10.2498/cit.1001268
IEEE
C. Aswani Kumar i S. Srinivas, "On the Performance of Latent Semantic Indexing-based Information Retrieval", Journal of computing and information technology, vol.17, br. 3, str. 259-264, 2009. [Online]. https://doi.org/10.2498/cit.1001268

Sažetak
Conventional vector based Information Retrieval (IR) models, Vector Space Model (VSM) and Generalized Vector Space Model (GVSM), represents documents and queries as vectors in a multidimensional space. This high dimensional data places great demands for computing resources. To overcome these problems, Latent Semantic Indexing (LSI): a variant of VSM, projects the documents into a lower dimensional space, computed via Singular Value Decomposition. It is stated in IR literature that LSI model is 30% more effective than classical VSM models. However statistical significance tests are required to evaluate the reliability of such comparisons. But to the best of our knowledge significance of performance of LSI model is not analyzed so far. Focus of this paper is to address this issue. We discuss the tradeoffs of VSM, GVSM and LSI and empirically evaluate the difference in performance on four testing document collections. Then we analyze the statistical significance of these performance differences.

Hrčak ID: 44864

URI
https://hrcak.srce.hr/44864

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