An Empirical Study on the Automatic Resolution of Semantic Ambiguity in Social Tags
ASIST 2011 Annual Meeting
New Orleans, LA, October 9-12, 2011
Due to the popularity of collaborative tagging services and systems, the role of social tags is critical in information organization and retrieval within the tagging system. Thus, the resolution of semantic ambiguity of tag sense (i.e., meaning) is important to the enhancement of information organization and retrieval in collaborative tagging applications. Our approach to tackle the task of the automatic resolution of semantic ambiguity is based on the hypothesis that given a target tag, some of the co-occurring social tags that were selectively assigned by the same or by different people to the same resource can serve as a useful dataset. In this paper, we propose three methods for tag sense disambiguation, aiming to automatically rank the senses associated with a given target tag. Experimental results on a Delicious dataset encourage the proposed methods. Pros and cons of the proposed methods will be discussed with the results.
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