CONF grangier:2005:nips_workshop/IDIAP Exploiting Hyperlinks to Learn a Retrieval Model Grangier, David Bengio, Samy EXTERNAL http://publications.idiap.ch/attachments/reports/2005/grangier-nips-ranking-workshop.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/grangier:2005:idiap-05-21 Related documents NIPS Workshop on Learning to Rank 2005 Whistler, Canada December 2005 12-17 Information Retrieval (IR) aims at solving a ranking problem: given a query $q$ and a corpus $C$, the documents of $C$ should be ranked such that the documents relevant to $q$ appear above the others. This task is generally performed by ranking the documents $d \in C$ according to their similarity with respect to $q$, $sim (q,d)$. The identification of an effective function $a,b \to sim(a,b)$ could be performed using a large set of queries with their corresponding relevance assessments. However, such data are especially expensive to label, thus, as an alternative, we propose to rely on hyperlink data which convey analogous semantic relationships. We then empirically show that a measure $sim$ inferred from hyperlinked documents can actually outperform the state-of-the-art {\em Okapi} approach, when applied over a non-hyperlinked retrieval corpus. 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.