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			<subfield code="a">grangier:2005:idiap-05-21/IDIAP</subfield>
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			<subfield code="a">Inferring Document Similarity from Hyper-links</subfield>
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			<subfield code="a">Grangier, David</subfield>
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			<subfield code="a">Bengio, Samy</subfield>
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			<subfield code="u">http://publications.idiap.ch/attachments/reports/2005/grangier-rr-05-21.pdf</subfield>
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			<subfield code="a">Idiap-RR-21-2005</subfield>
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			<subfield code="a">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.</subfield>
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