Accession Number:

ADA517813

Title:

UCSC at Relevance Feedback Track

Descriptive Note:

Conference paper

Corporate Author:

CALIFORNIA UNIV SANTA CRUZ SCHOOL OF ENGINEERING

Report Date:

2009-11-01

Pagination or Media Count:

6.0

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.

Subject Categories:

  • Information Science
  • Computer Programming and Software
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE