Accession Number:

ADA517685

Title:

Pairwise Document Classification for Relevance Feedback

Descriptive Note:

Conference paper

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA LANGUAGE TECHNOLOGIES INST

Report Date:

2009-11-01

Pagination or Media Count:

7.0

Abstract:

In this paper we present Carnegie Mellon Universitys 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.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE