Pairwise Document Classification for Relevance Feedback

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.