Generating automatically labeled data for author name disambiguation: an iterative clustering method
Abstract
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 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...
Paper Details
Title
Generating automatically labeled data for author name disambiguation: an iterative clustering method
Published Date
Jan 1, 2019
Journal
Volume
118
Issue
1
Pages
253 - 280
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