UMass Amherst and UT Austin @ The TREC 2009 Relevance Feedback Track

Abstract

We present a new supervised method for estimating term-based retrieval models and apply it to weight expansion terms from relevance feedback. While previous work on supervised feedback [Cao et al., 2008] demonstrated significantly improved retrieval accuracy over standard unsupervised approaches [Lavrenko and Croft, 2001, Zhai and Laerty, 2001], feedback terms were assumed to be independent in order to reduce training time. In contrast, we adapt the AdaRank learning algorithm [Xu and Li, 2007] to simultaneously estimate parameterization of all feedback terms. While not evaluated here, the method can be more generally applied for joint estimation of both query and feedback terms. To apply our method to a large web collection, we also investigate use of sampling to reduce feature extraction time while maintaining robust learning.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517782

Entities

People

  • Jangwon Seo
  • Marc-allen Cartright
  • Matthew Lease

Organizations

  • University of Massachusetts Amherst

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Coefficients
  • Data Mining
  • Extraction
  • Feature Extraction
  • Feedback
  • Information Retrieval
  • Information Science
  • Judgment
  • Knowledge Management
  • Language
  • Machine Learning
  • Mobile Phones
  • Standards
  • Statistics
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Information Retrieval
  • Neural Network Machine Learning.

Technology Areas

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