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Using Random Indexing to improve Singular Value Decomposition for Latent Semantic Analysis
Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.ORCID iD: 0000-0003-4899-588X
2008 (English)In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08) / [ed] Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Daniel Tapias, Marrakech, Morocco: European Language Resources Association, 2008Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present results from using Random indexing for Latent Semantic Analysis to handle Singular Value Decomposition tractability issues. In the paper we compare Latent Semantic Analysis, Random Indexing and Latent Semantic Analysis on Random Indexing reduced matrices. Our results show that Latent Semantic Analysis on Random Indexing reduced matrices provide better results on Precision and Recall than Random Indexing only. Furthermore, computation time for Singular Value Decomposition on a Random indexing reduced matrix is almost halved compared to Latent Semantic Analysis.

Place, publisher, year, edition, pages
Marrakech, Morocco: European Language Resources Association, 2008.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-43711Local ID: 74581ISBN: 2-9517408-4-0 (print)OAI: oai:DiVA.org:liu-43711DiVA: diva2:264571
Conference
6th International Conference on Language Resources and Evaluation, Marrakech, Morocco, May 26 - June 1, 2008.
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-01-13

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Jönsson, Arne

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  • sv-SE
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Output format
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