@inproceedings{vadrevu-etal-2021-xer,
    title = "x{ER}: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem",
    author = "Vadrevu, Samhita  and
      Nagi, Rakesh  and
      Xiong, JinJun  and
      Hwu, Wen-mei",
    editor = "Pruksachatkun, Yada  and
      Ramakrishna, Anil  and
      Chang, Kai-Wei  and
      Krishna, Satyapriya  and
      Dhamala, Jwala  and
      Guha, Tanaya  and
      Ren, Xiang",
    booktitle = "Proceedings of the First Workshop on Trustworthy Natural Language Processing",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.trustnlp-1.5/",
    doi = "10.18653/v1/2021.trustnlp-1.5",
    pages = "34--44",
    abstract = "In this paper, we propose a global, self- explainable solution to solve a prominent NLP problem: Entity Resolution (ER). We formu- late ER as a graph partitioning problem. Every mention of a real-world entity is represented by a node in the graph, and the pairwise sim- ilarity scores between the mentions are used to associate these nodes to exactly one clique, which represents a real-world entity in the ER domain. In this paper, we use Clique Partition- ing Problem (CPP), which is an Integer Pro- gram (IP) to formulate ER as a graph partition- ing problem and then highlight the explainable nature of this method. Since CPP is NP-Hard, we introduce an efficient solution procedure, the xER algorithm, to solve CPP as a combi- nation of finding maximal cliques in the graph and then performing generalized set packing using a novel formulation. We discuss the advantages of using xER over the traditional methods and provide the computational exper- iments and results of applying this method to ER data sets."
}