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Exploring author name disambiguation on PubMed-scale

Author

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  • Song, Min
  • Kim, Erin Hea-Jin
  • Kim, Ha Jin

Abstract

Author name disambiguation (AND) creates a daunting challenge in that disambiguation techniques often draw false conclusions when applied to incomplete or incorrect publication data. It becomes a more critical issue in the biomedical domain where PubMed articles are written by a wide range of researchers internationally. To tackle this issue, we create a carefully hand-crafted training set drawn from the entire PubMed collection by going through multiple iterations. We assess the quality of our training set by comparing it with SCOPUS-based training set. In addition, for the performance enhancement of the AND techniques, we propose a new set of publication features extracted by text mining techniques. The results of the experiments show that all four supervised learning techniques (Random Forest, C4.5, KNN, and SVM) with the new publication features (called NER model) achieve improved performance over the baseline and hybrid edit distance model.

Suggested Citation

  • Song, Min & Kim, Erin Hea-Jin & Kim, Ha Jin, 2015. "Exploring author name disambiguation on PubMed-scale," Journal of Informetrics, Elsevier, vol. 9(4), pages 924-941.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:4:p:924-941
    DOI: 10.1016/j.joi.2015.08.004
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    References listed on IDEAS

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    Cited by:

    1. Li Zhang & Wei Lu & Jinqing Yang, 2023. "LAGOS‐AND: A large gold standard dataset for scholarly author name disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 168-185, February.
    2. Humaira Waqas & Abdul Qadir, 2022. "Completing features for author name disambiguation (AND): an empirical analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 1039-1063, February.
    3. Ciriaco Andrea D’Angelo & Nees Jan Eck, 2020. "Collecting large-scale publication data at the level of individual researchers: a practical proposal for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 883-907, May.
    4. Jinseok Kim & Jinmo Kim & Jason Owen-Smith, 2019. "Generating automatically labeled data for author name disambiguation: an iterative clustering method," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 253-280, January.
    5. Jinseok Kim & Jason Owen-Smith, 2021. "ORCID-linked labeled data for evaluating author name disambiguation at scale," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2057-2083, March.
    6. Mehmet Ali Abdulhayoglu & Bart Thijs, 2017. "Use of ResearchGate and Google CSE for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1965-1985, June.
    7. Jinseok Kim & Jenna Kim & Jason Owen‐Smith, 2021. "Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 979-994, August.
    8. Jinseok Kim & Jenna Kim, 2020. "Effect of forename string on author name disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(7), pages 839-855, July.
    9. Wang, Zhenhua & Ren, Ming & Gao, Dong & Li, Zhuang, 2023. "A Zipf's law-based text generation approach for addressing imbalance in entity extraction," Journal of Informetrics, Elsevier, vol. 17(4).

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