On updating problems in latent semantic indexing

PDF Version Also Available for Download.

Description

The authors develop new SVD-updating algorithms for three types of updating problems arising from Latent Semantic Indexing (LSI) for information retrieval to deal with rapidly changing text document collections. They also provide theoretical justification for using a reduced-dimension representation of the original document collection in the updating process. Numerical experiments using several standard text document collections show that the new algorithms give higher (interpolated) average precisions than the existing algorithms and the retrieval accuracy is comparable to that obtained using the complete document collection.

Physical Description

12 p.

Creation Information

Simon, H.D. & Zha, H. November 1, 1997.

Context

This report is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 23 times . More information about this report can be viewed below.

Who

People and organizations associated with either the creation of this report or its content.

Authors

  • Simon, H.D. Lawrence Berkeley National Lab., CA (United States)
  • Zha, H. Pennsylvania State Univ., University Park, PA (United States). Dept. of Computer Science and Engineering

Sponsors

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this report. Follow the links below to find similar items on the Digital Library.

Description

The authors develop new SVD-updating algorithms for three types of updating problems arising from Latent Semantic Indexing (LSI) for information retrieval to deal with rapidly changing text document collections. They also provide theoretical justification for using a reduced-dimension representation of the original document collection in the updating process. Numerical experiments using several standard text document collections show that the new algorithms give higher (interpolated) average precisions than the existing algorithms and the retrieval accuracy is comparable to that obtained using the complete document collection.

Physical Description

12 p.

Notes

OSTI as DE98052316

Source

  • Other Information: PBD: Nov 1997

Language

Item Type

Identifier

Unique identifying numbers for this report in the Digital Library or other systems.

  • Other: DE98052316
  • Report No.: LBNL--41101
  • Grant Number: AC03-76SF00098
  • DOI: 10.2172/650342 | External Link
  • Office of Scientific & Technical Information Report Number: 650342
  • Archival Resource Key: ark:/67531/metadc702781

Collections

This report is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this report?

When

Dates and time periods associated with this report.

Creation Date

  • November 1, 1997

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

Description Last Updated

  • Aug. 23, 2016, 3:13 p.m.

Usage Statistics

When was this report last used?

Yesterday: 0
Past 30 days: 3
Total Uses: 23

Interact With This Report

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Simon, H.D. & Zha, H. On updating problems in latent semantic indexing, report, November 1, 1997; California. (digital.library.unt.edu/ark:/67531/metadc702781/: accessed December 10, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.