A Language Modeling Framework for Selective Query Expansion

Abstract

Query expansion is a well-known technique that has been shown to improve average retrieval performance. This technique has not been used in many operational systems because of the fact that it can greatly degrade the performance of some individual queries. We show how comparison between language models of the unexpanded and expanded retrieval results can be used to predict when the expanded retrieval has strayed from the original sense of the query. In these cases, the unexpanded results are used while the expanded results are used in the remaining cases (where such straying is not detected). We evaluate this method and others on a wide variety of TREC collections and show how to automatically compute a decision threshold for a collection. We demonstrate the ability of the method to enhance the effectiveness and reliability of the query expansion technique in information retrieval.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA478016

Entities

People

  • Steve Cronen-townsend
  • W. Bruce Croft
  • Yun Zhou

Organizations

  • University of Massachusetts Amherst

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  • Abstracts
  • Automatic
  • Carbon Monoxide
  • Computer Science
  • Dielectric Gases
  • Diseases And Disorders
  • Fluids
  • Information Retrieval
  • Language
  • Lyme Disease
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  • Probability Distributions
  • Standards
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Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Information Retrieval

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval