@inproceedings{5047df95784b43f588021e5553f9ed35,

title = "XER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem",

abstract = "In this paper, we propose a global, selfexplainable solution to solve a prominent NLP problem: Entity Resolution (ER). We formulate ER as a graph partitioning problem. Every mention of a real-world entity is represented by a node in the graph, and the pairwise similarity 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 Partitioning Problem (CPP), which is an Integer Program (IP) to formulate ER as a graph partitioning 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 combination 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 experiments and results of applying this method to ER data sets.",

author = "Samhita Vadrevu and Hwu, {Wen Mei} and Rakesh Nagi and Jinjun Xiong",

note = "Funding Information: This work is supported by the IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) - a research collaboration as a part of the IBM AI Horizons Network. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; 1st Workshop on Trustworthy Natural Language Processing, TrustNLP 2021 ; Conference date: 10-06-2021",

year = "2021",

language = "English (US)",

series = "TrustNLP 2021 - 1st Workshop on Trustworthy Natural Language Processing, Proceedings of the Workshop",

publisher = "Association for Computational Linguistics (ACL)",

pages = "34--44",

editor = "Yada Pruksachatkun and Anil Ramakrishna and Kai-Wei Chang and Satyapriya Krishna and Jwala Dhamala and Tanaya Guha and Xiang Ren",

booktitle = "TrustNLP 2021 - 1st Workshop on Trustworthy Natural Language Processing, Proceedings of the Workshop",

}