Mining Specific and General Features in Both Positive and Negative Relevance Feedback. QUT E-Discovery Lab at the TREC'09 Relevance Feedback Track

Abstract

User relevance feedback is usually utilized by Web systems to interpret user information needs and retrieve effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovery knowledge are two critical problems. In TREC 2009, we participated in the Relevance Feedback Track and experimented a model consisting of two innovative stages: one for subject-based query expansion to extract pseudo-relevance feedback; one for relevance feature discovery to find useful patterns and terms in relevance judgements to rank documents. In this paper, the detailed description of our model is given, as well as the related discussions for the experimental results.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517808

Entities

People

  • Abdulmohsen Algarni
  • Sheng-tang Wu
  • Xiaohui Tao
  • Yuefeng Li

Organizations

  • University of Queensland

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Acquisition
  • Algorithms
  • Data Sets
  • Economics
  • Education
  • Feedback
  • Filtration
  • Information Retrieval
  • Information Science
  • Judgment
  • Models
  • Ontologies
  • Supervised Machine Learning
  • Taxonomy
  • Test And Evaluation
  • Training

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Information Retrieval
  • Instructional Design and Training Evaluation.