Incorporating Relevance and Psuedo-Relevance Feedback in the Markov Random Field Model

Abstract

We present a new document retrieval approach combining relevance feedback, pseudo-relevance feedback, and Markov random field modeling of term interaction. Overall effectiveness of our combined model and the relative contribution from each component is evaluated on the GOV2 webpage collection. Given 0-5 feedback documents, we find each component contributes unique value to the overall ensemble, achieving significant improvement individually and in combination. Comparative evaluation in the 2008 TREC Relevance Feedback track further shows our complete system typically performs as well or better than peer systems.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2008
Accession Number
ADA512669

Entities

People

  • Matthew Lease

Organizations

  • Brown University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computer Science
  • Equations
  • Feedback
  • Frequency
  • Information Processing
  • Information Retrieval
  • Information Science
  • Language
  • Precision
  • Probability
  • Random Variables
  • Standards
  • Statistics
  • Test And Evaluation
  • Test Sets

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Information Retrieval