  To train algorithms for supervised author name disambiguation, many studies
have relied on hand-labeled truth data that are very laborious to generate.
This paper shows that labeled training data can be automatically generated
using information features such as email address, coauthor names, and cited
references that are available from publication records. For this purpose,
high-precision rules for matching name instances on each feature are decided
using an external-authority database. Then, selected name instances in target
ambiguous data go through the process of pairwise matching based on the rules.
Next, they are merged into clusters by a generic entity resolution algorithm.
The clustering procedure is repeated over other features until further merging
is impossible. Tested on 26,566 instances out of the population of 228K author
name instances, this iterative clustering produced accurately labeled data with
pairwise F1 = 0.99. The labeled data represented the population data in terms
of name ethnicity and co-disambiguating name group size distributions. In
addition, trained on the labeled data, machine learning algorithms
disambiguated 24K names in test data with performance of pairwise F1 = 0.90 ~
0.92. Several challenges are discussed for applying this method to resolving
author name ambiguity in large-scale scholarly data.
