  Author name disambiguation in bibliographic databases is the problem of
grouping together scientific publications written by the same person,
accounting for potential homonyms and/or synonyms. Among solutions to this
problem, digital libraries are increasingly offering tools for authors to
manually curate their publications and claim those that are theirs. Indirectly,
these tools allow for the inexpensive collection of large annotated training
data, which can be further leveraged to build a complementary automated
disambiguation system capable of inferring patterns for identifying
publications written by the same person. Building on more than 1 million
publicly released crowdsourced annotations, we propose an automated author
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
phonetic-based blocking strategies, thereby increasing recall; and (ii) adding
strong ethnicity-sensitive features for learning a linkage function, thereby
tailoring disambiguation to non-Western author names whenever necessary.
