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@TECHREPORT{grangier:2005:idiap-05-21,
                      author = {Grangier, David and Bengio, Samy},
                    projects = {Idiap},
                       title = {Inferring Document Similarity from Hyper-links},
                        type = {Idiap-RR},
                      number = {Idiap-RR-21-2005},
                        year = {2005},
                 institution = {IDIAP},
                    abstract = {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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2005/grangier-rr-05-21.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2005/grangier-rr-05-21.ps.gz},
ipdmembership={speech},
}