UCSC at Relevance Feedback Track

Abstract

The relevance feedback track in TREC 2009 focuses on two sub tasks: actively selecting good documents for users to provide relevance feedback and retrieving documents based on user relevance feedback. For the first task, we tried a clustering based method and the Transductive Experimental Design (TED) method proposed by Yu et al.. For clustering based method, we use the K-means algorithm to cluster the top retrieved documents and choose the most representative document of each cluster. The TED method aims to find documents that are hard-to-predict and representative of the unlabeled documents. For the second task, we did query expansion based on a relevance model learned on the relevant documents.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517813

Entities

People

  • Jadiel De Arma
  • Kai Yu
  • Lanbo Zhang
  • Yi Zhang

Organizations

  • University of California, Santa Cruz

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Clustering
  • Computer Science
  • Data Sets
  • Experimental Design
  • Failure Analysis
  • Feedback
  • Information Operations
  • Information Retrieval
  • Language
  • Mathematics
  • Standards
  • Word Lists

Fields of Study

  • Computer science

Readers

  • Information Retrieval
  • Neural Network Machine Learning.