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

arXiv: https://arxiv.org/abs/2102.03272 · HTML: https://mlsanity.com/paper/2102.03272/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [Ethnicity sensitive author disambiguation using semi-supervised learning](https://mlsanity.com/paper/1508.07744/) (2015)
- [Exploiting citation networks for large-scale author name disambiguation](https://mlsanity.com/paper/1401.6157/) (2014)
- [Distortive Effects of Initial-Based Name Disambiguation on Measurements of Large-Scale Coauthorship Networks](https://mlsanity.com/paper/1502.06306/) (2015)
- [Accuracy of simple, initials-based methods for author name disambiguation](https://mlsanity.com/paper/1308.0749/) (2013)
- [Evaluating author name disambiguation for digital libraries: A case of DBLP](https://mlsanity.com/paper/1806.10540/) (2018)
- [The impact of imbalanced training data on machine learning for author name disambiguation](https://mlsanity.com/paper/1808.00525/) (2018)
- [A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments](https://mlsanity.com/paper/1812.08538/) (2018)

## Built on by (most cited first, 1 of 1)

- [Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives](https://mlsanity.com/paper/2212.12715/) (2022)

## Abstract (arXiv)

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.