Summary of the paper

Title Using Random Indexing to improve Singular Value Decomposition for Latent Semantic Analysis
Authors Linus Sellberg and Arne Jönsson
Abstract 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.
Language Language-independent
Topics Information Extraction, Information Retrieval, Question Answering, Text mining
Full paper Using Random Indexing to improve Singular Value Decomposition for Latent Semantic Analysis
Slides -
Bibtex @InProceedings{SELLBERG08.586,
  author = {Linus Sellberg and Arne Jönsson},
  title = {Using Random Indexing to improve Singular Value Decomposition for Latent Semantic Analysis},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {http://www.lrec-conf.org/proceedings/lrec2008/},
  language = {english}
  }

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