Identity Uncertainty and Citation Matching

By Hanna M. Pasula, Bhaskara Marthi, Brian Milch, Stuart J. Russell, Ilya Shpitser. Neural Information Processing Systems, 2002

357 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/25308818aa94ffb3a9809540b530f6e8c7bfb83c

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Citations in the corpus

Adaptive Name Matching in Information Integration (2003, methodology): “Another avenue for using supervised learning to improve name-matching relies on creating a relational probabilistic model for the domain, where a generative model for individual fields is constructed, and a Markov Chain Monte Carlo procedure is used for obtaining the matching decisions [19].”
Two supervised learning approaches for name disambiguation in author citations (2004, background): “Name ambiguity is a special case of the general problem of identity uncertainty, where objects are not labeled with unique identifiers [30].”
Citation recommendation: approaches and datasets (2020, unknown): “ited papers can be obtained, opening the door for citation-based automatic survey generation and automatic related work section generation [2,49,114]. Citation matching and modeling Citation matching [123] deals with the research challenge of nding identical citations in dierent documents in order t”
Reference reconciliation in complex information spaces (2005, background): “In [31], a complex generative model is proposed that captures dependencies between various classes and attributes and also possible errors during reference extraction.”
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.”
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].”
A Comparison of String Metrics for Matching Names and Records (2003, methodology): “More recently, probabilistic object identification methods have been adapted to matching tasks (Pasula et al. 2002).”
A Latent Dirichlet Model for Unsupervised Entity Resolution (2005, background): “Recently, generative [22, 29] and discriminative [24, 28] probabilistic approaches have been proposed as well as non-probabilistic algorithms [20, 12].”
Probabilistic models with unknown objects (2006, methodology): “However, it is often necessary to develop a special-purpose proposal distribution to achieve fast convergence on a particular task [Tu and Zhu, 2002; Pasula et al., 2003; Oh et al., 2004].”
Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “Example domains include natural language processing (Manning and Schütze, 1999), speech recognition (Baker, 1979), information extraction (Pasula et al., 2002), multitarget tracking and sensor fusion (Oh et al., 2009; Arora et al., 2010), ecology (Csilléry et al., 2010) and computational biology…”
Unifying logic and probability (2015, unknown): “Given citation strings as evidence, a multi-author version of this model, trained in an unsupervised fashion, had an error rate 2 to 3 times lower than CiteSeer’s on four standard test sets [24].”
Dependency Tree Kernels for Relation Extraction (2004, background): “Coreference resolution is an active area of research not investigated here (Pasula et al., 2002; McCallum and Wellner, 2003).”
Joint Inference in Information Extraction (2007, methodology): “This is particularly notable given that the models of Pasula et al. (2003) and Wellner et al. (2004) involved considerably more knowledge engineering than ours, contained more learnable parameters, and used additional training data.”
A Comparison of String Distance Metrics for Name-Matching Tasks (2003, methodology): “More recently, probabilistic object identification methods have been adapted to matching tasks (Pasula et al. 2002).”
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016, methodology): “is arises in citation matching [Pasula et al., 2003] when we need to distinguish whether two references refer to the same paper and in record linkage [Fellegi and Sunter, 1969], where the aim is to determine if two hospital records refer to the same person (e.”