Pairwise Document Classification for Relevance Feedback

Abstract

In this paper we present Carnegie Mellon University's submission to the TREC 2009 Relevance Feedback Track. In this submission we take a classification approach on document pairs to using relevance feedback information. We explore using textual and non-textual document-pair features to classify unjudged documents as relevant or non-relevant, and use this prediction to re-rank a baseline document retrieval. These features include co-citation measures, URL similarities, as well as features often used in machine learning systems for document ranking such as the difference in scores assigned by the baseline retrieval system.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517685

Entities

People

  • Jaime Carbonell
  • Jamie Callan
  • Jonathan L. Elsas
  • Pinar Donmez

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Celestial Brightness
  • Classification
  • Clustering
  • Coefficients
  • Feedback
  • Information Operations
  • Information Science
  • Judgment
  • Language
  • Learning
  • Machine Learning
  • Natural Language Processing
  • Residuals
  • Standards
  • Training

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Information Retrieval

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks