Inferring Document Similarity from Hyperlinks
| Type of publication: | Conference paper |
| Citation: | grangier:2005:cikm |
| Booktitle: | ACM Conference on Information and Knowledge Management |
| Year: | 2005 |
| Month: | 10 |
| Address: | Bremen, Germany |
| Crossref: | grangier:2005:idiap-05-21: |
| Abstract: | 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. |
| Userfields: | ipdmembership={speech}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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