Combining Classifiers and User Feedback for Disambiguating Author Names

Historically, supervised methods have been the most effective ones for author name disambiguation tasks. In here, we propose a specific manner to combine supervised techniques along with user feedback. Although, we use supervised techniques, the only user effort is to provide feedback on results since initial training data is automatically generated. Our experiments show gains up to 20% in the disambiguation performance against representative baselines.