CONF grangier:2005:cikm/IDIAP Inferring Document Similarity from Hyperlinks Grangier, David Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2005/grangier-2005-cikm.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/grangier:2005:idiap-05-21 Related documents ACM Conference on Information and Knowledge Management 2005 Bremen, Germany October 2005 359-360 Assessing semantic similarity between text documents is a crucial aspect in Information Retrieval systems. In this work, we propose to use hyperlink information to derive a similarity measure that can then be applied to compare any text documents, with or without hyperlinks. As linked documents are generally semantically closer than unlinked documents, we use a training corpus with hyperlinks to infer a function $a,b \to sim(a,b)$ that assigns a higher value to linked documents than to unlinked ones. Two sets of experiments on different corpora show that this function compares favorably with {\em OKAPI} matching on document retrieval tasks. REPORT grangier:2005:idiap-05-21/IDIAP Inferring Document Similarity from Hyper-links Grangier, David Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2005/grangier-rr-05-21.pdf PUBLIC Idiap-RR-21-2005 2005 IDIAP Assessing semantic similarity between text documents is a crucial aspect in Information Retrieval systems. In this paper, we propose a technique to derive a similarity measure from hyper-link information. As linked documents are generally semantically closer than unlinked documents, we use a training corpus with hyper-links to infer a function $a,b \to sim(a,b)$ that assigns a higher value to linked documents than to unlinked ones. Two sets of experiments on different corpora show that this function compares favorably with {\em OKAPI} matching on document retrieval tasks.