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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Journal of the American Society for Information Science and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.