A General Framework for Personalized Text Classification and Annotation.
Andrea Baruzzo, Antonina Dattolo, Nirmala Pudota, Carlo Tasso · 2009
The tremendous volume of digital contents available today on the Web and the rapid spread of Web 2.0 sites, blogs and forums have exacerbated the classical information overload problem. Moreover, they have made even worse the challenge of finding new content appropriate to individual needs. In order to alleviate these issues, new approaches and tools are needed to provide personalized content recommendations and classification schemata. This paper presents the PIRATES framework: a Personalized Intelligent Recommender and Annotator TEStbed for text-based content retrieval and classification. Using an integrated set of tools, this framework lets the users experiment, customize, and personalize the way they retrieve, filter, and organize the large amount of information available on the Web. Furthermore, the PIRATES framework undertakes a novel approach that automates typical manual tasks such as content annotation and tagging, by means of personalized tags recommendations and other forms of tex- tual annotations (e.g. key-phrases).