BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting

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 1 .