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Llorente, Ainhoa; Motta, Enrico and Rüger, Stefan
(2009).
DOI: https://doi.org/10.1007/978-3-642-15751-6_40
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.
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About
- Item ORO ID
- 23432
- Item Type
- Conference or Workshop Item
- ISSN
- 0302-9743
- Extra Information
- The original publication is available at www.springerlink.com
- Academic Unit or 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)
- Copyright Holders
- © 2010 Springer-Verlag
- Depositing User
- Kay Dave