Pairwise Document Classification for Relevance Feedback
Date of Original Version
Abstract or Table of Contents
In this paper we present Carnegie Mellon University's submission to the TREC 2009 Relevance Feedback Track. In this submission we take a classi cation 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.