Generating automatically labeled data for author name disambiguation: An iterative clustering method

TL;DR

This paper presents an iterative clustering method that automatically generates high-quality labeled data for author name disambiguation using publication record features, reducing manual labeling effort.

Contribution

It introduces a novel iterative clustering approach leveraging external-authority data and multiple features to automatically produce labeled datasets for author disambiguation.

Findings

Achieved pairwise F1 = 0.99 on labeled data

Disambiguated 24K names with F1 = 0.90-0.92

Demonstrated effectiveness in large-scale scholarly data

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 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.