BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting
<|reference_start|>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.<|reference_end|>
@article{cheng2024bond:,
    title={BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task
  Promoting},
    author={Yuqing Cheng, Bo Chen, Fanjin Zhang, Jie Tang},
    journal={Proceedings of TheWebConf 2024 (WWW '24), May 13--17, 2024,
  Singapore},
    year={2024},
    doi={10.1145/3589334.3645580.},
    archivePrefix={arXiv},
    eprint={2404.08322},
    primaryClass={cs.SI cs.AI}
}