Citation-based bootstrapping for large-scale author disambiguation

Michael Levin, Stefan Krawczyk, Steven Bethard, Dan Jurafsky

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

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 B 3 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.

Original languageEnglish (US)
Pages (from-to)1030-1047
Number of pages18
JournalJournal of the American Society for Information Science and Technology
Volume63
Issue number5
DOIs
StatePublished - May 2012
Externally publishedYes

Keywords

  • automatic extracting
  • disambiguation
  • machine aided indexing

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence

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