Inferring Document Similarity from Hyper-links
| Type of publication: | Idiap-RR |
| Citation: | grangier:2005:idiap-05-21 |
| 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. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Crossref by |
grangier:2005:nips_workshop grangier:2005:cikm |
| Added by: | [UNK] |
| Total mark: | 0 |
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