BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting
arxiv(2024)
摘要
From-scratch name disambiguation is an essential task for establishing a
reliable foundation for academic platforms. It involves partitioning documents
authored by identically named individuals into groups representing distinct
real-life experts. Canonically, the process is divided into two decoupled
tasks: locally estimating the pairwise similarities between documents followed
by globally grouping these documents into appropriate clusters. However, such a
decoupled approach often inhibits optimal information exchange between these
intertwined tasks. Therefore, we present BOND, which bootstraps the local and
global informative signals to promote each other in an end-to-end regime.
Specifically, BOND harnesses local pairwise similarities to drive global
clustering, subsequently generating pseudo-clustering labels. These global
signals further refine local pairwise characterizations. The experimental
results establish BOND's superiority, outperforming other advanced baselines by
a substantial margin. Moreover, an enhanced version, BOND+, incorporating
ensemble and post-match techniques, rivals the top methods in the WhoIsWho
competition.
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