Llorente, Ainhoa; Motta, Enrico and Rüger, Stefan
(2009).
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(Version of Record)
Due to publisher licensing restrictions, this file is not available for public download |
| DOI (Digital Object Identifier) Link: | https://doi.org/10.1007/978-3-642-15751-6_40 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
The goal of this research is to explore several semantic relatedness measures that help to refine annotations generated by a baseline non-parametric density estimation algorithm. Thus, we analyse the benefits of performing a statistical correlation using the training set or using the World Wide Web versus approaches based on a thesaurus like WordNet or Wikipedia (considered as a hyperlink structure). Experiments are carried out using the dataset provided by the 2009 edition of the ImageCLEF competition, a subset of the MIR-Flickr 25k collection. Best results correspond to approaches based on statistical correlation as they do not depend on a prior disambiguation phase like WordNet and Wikipedia. Further work needs to be done to assess whether proper disambiguation schemas might improve their performance.
| Item Type: | Conference or Workshop Item |
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| Copyright Holders: | 2010 Springer-Verlag |
| ISSN: | 0302-9743 |
| Extra Information: | The original publication is available at www.springerlink.com |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Research Group: | Centre for Research in Computing (CRC) |
| Item ID: | 23432 |
| Depositing User: | Kay Dave |
| Date Deposited: | 05 Oct 2010 10:34 |
| Last Modified: | 08 Dec 2018 13:41 |
| URI: | http://oro.open.ac.uk/id/eprint/23432 |
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