A Keyphrase Extraction Approach for Social Tagging Systems

Social tagging systems allow people to classify resources by using a set of freely chosen terms named tags. However, by shifting the classification task from a set of experts to a larger and not trained set of people, the results of the classification are not accurate. The lack of control and guidelines generates noisy tags (i.e. tags without a clear semantics) which deteriorate the precision of the user generated classifications. In order to face this limitation several tools have been proposed in the literature for suggesting to the users tags which properly describe a given resource. In this paper we propose to suggest n-grams (named keyphrases) by following the idea that sequences of two/three terms can better face potential ambiguities. More specifically, in this work, we identify a set of features which characterize n-grams able to describe meaningful aspects reported in Web pages. By means of these features we developed a mechanism which can support people to manually classify Web pages by automatically suggesting meaningful keyphrases expressed in English.