A Latent Dirichlet Model for Unsupervised Entity Resolution

By Indrajit Bhattacharya, Lise Getoor. SDM, 2005

297 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/40df4771b203dc7b7f1b959745b88a44adefbfcc

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In Management Science and Operations Research

Citations in the corpus

Large-Scale Deduplication with Constraints Using Dedupalog (2009, background): “As a result, many clustering algorithms do incorporate constraints [2], [3], [4], [5], [6], [7], [8].”
Author Name Disambiguation in MEDLINE (2009, methodology): “One could also explore modeling techniques that go beyond triplets such as coauthorship groups [Bhattacharya and Getoor 2006; Song et al. 2007], aggregate constraints [Culotta et al. 2007], or so-called collective entity resolution [Bhattacharya and Getoor 2007].”
Learning to classify short and sparse text & web with hidden topics from large-scale data collections (2008, methodology): “Models like pLSA, LDA, and their variants have been having more successful applications in document & topic modeling [8, 19], dimensionality reduction for text categorization [8], collaborative filtering [23], ad hoc IR [33], entity resolution [7], and digital library [9], and many more.”
Adaptive Blocking: Learning to Scale Up Record Linkage (2006, methodology): “For example, in record linkage (also known as object identification [35], de-duplication [31], entity matching [9, 33, 2] and identity uncertainty [29, 23]), similarity must be computed between all pairs of records to identify groups of records that refer to the same underlying entity.”
Collective entity resolution in relational data (2007, methodology): “This article builds on our initial work on entity resolution in relational data described in a workshop paper [Bhattacharya and Getoor 2004] and included in a survey book chapter [Bhattacharya and Getoor 2006a].”
Frameworks for entity matching: A comparison (2010, background): “Context matchers commonly represent contextual information (e.g., semantic relationships, hierarchies) in a graph structure, see for example [16] [34] [7] [6] [15] [21] [22] [51].”
Large-Scale Collective Entity Matching (2011, methodology): “Such state-of-the-art EM tools rely on various various advanced probabilistic models, including Conditional Random Fields (CRFs) [16, 6], relational Bayesian networks [19], latent Dirichlet models [3, 11], and Markov Logic Networks [21].”
Joint Entity Resolution (2012, unknown): “[4] proposes unsupervised learning techniques for ER.”
Latent dirichlet allocation for tag recommendation (2009, methodology): “[10] and applied to solve various tasks including topic identification [16], entity resolution [7], and Web spam classification [8].”