Web: http://arxiv.org/abs/2205.05889

May 13, 2022, 1:11 a.m. | Tianshu Wang, Hongyu Lin, Cheng Fu, Xianpei Han, Le Sun, Feiyu Xiong, Hui Chen, Minlong Lu, Xiuwen Zhu

cs.CL updates on arXiv.org arxiv.org

Entity matching (EM) is the most critical step for entity resolution (ER).
While current deep learningbased methods achieve very impressive performance on
standard EM benchmarks, their realworld application performance is much
frustrating. In this paper, we highlight that such the gap between reality and
ideality stems from the unreasonable benchmark construction process, which is
inconsistent with the nature of entity matching and therefore leads to biased
evaluations of current EM approaches. To this end, we build a new EM corpus …

arxiv benchmark construction gap

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