CMIC@TREC-2009: Relevance Feedback Track

Abstract

This paper describes CMIC's submissions to the TREC'09 relevance feedback track. In the phase 1 runs we submitted, we experimented with two different techniques to produce 5 documents to be judged by the user in the initial feedback step, namely using knowledge bases and clustering. Both techniques attempt to topically diversify these 5 documents as much as possible in an effort to maximize the probability that they contain at least 1 relevant document. The basic premise is that if a query has n diverse interpretations, then diversifying results and picking the top 5 most likely interpretations would maximize the probability that a user would be interested in at least one interpretation. In phase 2 runs, which involved the use of the feedback attained from phase 1 judgments, we attempted to use positive and negative judgments in weighing the terms to be used for subsequent feedback.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517870

Entities

People

  • Ahmed El-deeb
  • Kareem Darwish

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Clustering
  • Feature Selection
  • Feedback
  • Information Operations
  • Judgment
  • Machine Learning
  • Precision
  • Probability
  • Standards
  • Taxonomy
  • Unsupervised Machine Learning

Readers

  • Information Retrieval
  • Regression Analysis.