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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2404.08322 |
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| _version_ | 1866911837431267328 |
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| author | Cheng, Yuqing Chen, Bo Zhang, Fanjin Tang, Jie |
| author_facet | Cheng, Yuqing Chen, Bo Zhang, Fanjin Tang, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08322 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting Cheng, Yuqing Chen, Bo Zhang, Fanjin Tang, Jie Social and Information Networks Artificial Intelligence H.3.7; H.3.3 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. |
| title | BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting |
| topic | Social and Information Networks Artificial Intelligence H.3.7; H.3.3 |
| url | https://arxiv.org/abs/2404.08322 |