Polynomial filtering in latent semantic indexing for information retrieval

E. Kokiopoulou, Yousef Saad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

Latent Semantic Indexing (LSI) is a well established and effective framework for conceptual information retrieval. In traditional implementations of LSI the semantic structure of the collection is projected into the k-dimensional space derived from a rank-k approximation of the original term-by-document matrix. This paper discusses a new way to implement the LSI methodology, based on polynomial filtering. The new framework does not rely on any matrix decomposition and therefore its computational cost and storage requirements are low relative to traditional implementations of LSI. Additionally, it can be used as an effective information filtering technique when updating LSI models based on user feedback.

Original languageEnglish (US)
Title of host publicationProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages104-111
Number of pages8
ISBN (Print)1581138814, 9781581138818
DOIs
StatePublished - 2004
EventProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Sheffield, United Kingdom
Duration: Jul 25 2004Jul 29 2004

Publication series

NameProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

OtherProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
CountryUnited Kingdom
CitySheffield
Period7/25/047/29/04

Keywords

  • Latent Semantic Indexing
  • Polynomial filtering

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