An empirical study on the automatic resolution of semantic ambiguity in social tags

Abstract

Due to the popularity of collaborative tagging services and systems, the role of social tags in information organization and retrieval has become increasingly critical within the tagging system. Consequently, the resolution of semantic ambiguity of tag sense (i.e., meaning) plays an important role in the enhancement of information organization and retrieval in collaborative tagging applications. Our approach to tackle the task of 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. Four different methods (i.e., two LSA-based, LIN-based, and Cooccurrence method) for tag sense disambiguation are proposed and their results are compared, aiming to automatically rank the senses associated with a given target tag. The experimental results with a Delicious dataset indicate that LSA-w and LIN-based methods produce more stable performances, and suggest the same two methods for the Tag Sense Disambiguation task, particularly in the situation that the Boolean effect is more likely to arise.