IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v63y2012i5p1030-1047.html
   My bibliography  Save this article

Citation‐based bootstrapping for large‐scale author disambiguation

Author

Listed:
  • Michael Levin
  • Stefan Krawczyk
  • Steven Bethard
  • Dan Jurafsky

Abstract

We present a new, two‐stage, self‐supervised algorithm for author disambiguation in large bibliographic databases. In the first “bootstrap” stage, a collection of high‐precision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature‐based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our self‐supervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters' Web of Knowledge with B3 F1 of.807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self‐citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from.748 to.767) suggests they may not be worth the trouble of extracting from databases that don't already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F1.

Suggested Citation

  • Michael Levin & Stefan Krawczyk & Steven Bethard & Dan Jurafsky, 2012. "Citation‐based bootstrapping for large‐scale author disambiguation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(5), pages 1030-1047, May.
    • Handle: RePEc:bla:jamist:v:63:y:2012:i:5:p:1030-1047
    DOI: 10.1002/asi.22621
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.22621
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.22621?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Gordon Rogers & Martin Szomszor & Jonathan Adams, 2020. "Sample size in bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 777-794, October.
    3. Jinseok Kim, 2019. "A fast and integrative algorithm for clustering performance evaluation in author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 661-681, August.
    4. 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.
    5. Humaira Waqas & Muhammad Abdul Qadir, 2021. "Multilayer heuristics based clustering framework (MHCF) for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7637-7678, September.
    6. 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.
    7. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jamist:v:63:y:2012:i:5:p:1030-1047. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.