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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 | citiraj | |
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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 15.12.2019. 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 15.12.2019.];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 |
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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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