Ethnicity Sensitive Author Disambiguation Using Semi-supervised Learning

Building on more than one million crowdsourced annotations that we publicly release, we propose a new automated disambiguation solution exploiting this data (i) to learn an accurate classifier for identifying coreferring authors and (ii) to guide the clustering of scientific publications by distinct authors in a semi-supervised way. To the best of our knowledge, our analysis is the first to be carried out on data of this size and coverage. With respect to the state of the art, we validate the general pipeline used in most existing solutions, and improve by: (i) proposing new phonetic-based blocking strategies, thereby increasing recall; (ii) adding strong ethnicity-sensitive features for learning a linkage function, thereby tailoring disambiguation to non-Western author names whenever necessary; and (iii) showing the importance of balancing negative and positive examples when learning the linkage function.