Abstract
This paper presents an approach for word sense disambiguation (WSD) of image tags from professional and social image databases without categorial labels, using WordNet as an external resource defining word senses. We focus on the resolution of lexical ambiguity that arises when a given keyword has several different meanings. Our approach combines some knowledge-based methods (Lesk algorithm and Hyponym Heuristics) and semantic measures, in order to achieve successful disambiguation. Experimental results and performance evaluation are 95.16 % accuracy for professional images and 87.40 % accuracy for social images, for keywords included in WordNet. This approach can be used for improving machine translation of tags or image similarity measurement.
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Acknowledgments
The research work presented in this paper is partially supported by the FP7 grant 316087 AComIn “Advanced Computing for Innovation”, funded by the European Commission in 2012–2016. It is also related to the COST Action IC1307 “Integrating Vision and Language (iV&L Net): Combining Computer Vision and Language Processing for Advanced Search, Retrieval, Annotation and Description of Visual Data”. The authors are thankful to Imagga company for their comments, recommendations and experimental datasets.
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Kanishcheva, O., Angelova, G. (2016). About Sense Disambiguation of Image Tags in Large Annotated Image Collections. In: Margenov, S., Angelova, G., Agre, G. (eds) Innovative Approaches and Solutions in Advanced Intelligent Systems . Studies in Computational Intelligence, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-32207-0_9
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DOI: https://doi.org/10.1007/978-3-319-32207-0_9
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