Ethnicity-based name partitioning for author name disambiguation using supervised machine learning.

ABSTRACT: In several author name disambiguation studies, some ethnic name groups such as East Asian names are reported to be more difficult to disambiguate than others. This implies that disambiguation approaches might be improved if ethnic name groups are distinguished before disambiguation. We explore the potential of ethnic name partitioning by comparing performance of four machine learning algorithms trained and tested on the entire data or specifically on individual name groups. Results show that ethnicity-based name partitioning can substantially improve disambiguation performance because the individual models are better suited for their respective name group. The improvements occur across all ethnic name groups with different magnitudes. Performance gains in predicting matched name pairs outw

SUBMITTER: Kim J 

PROVIDER: S-EPMC8359369 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature