Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rankbased in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach. Introduction Record deduplication is the problem of deciding whether two records in a database refer to the same object. This problem is widespread in any large-scale database, and is particularly acute when records are constructed automatically from text mining. Author disambiguation, the problem of deduplicating author records, is a critical concern for digital publication libraries such as Citeseer, DBLP, Rexa, and Google Scholar. Author disambiguation is difficult in these domains because of abbreviations (e.g., Y. Smith) misspellings (e.g., Y. Smiht), and extraction errors (e.g., Smith Statistical). Many supervised machine learning approaches to author disambiguation have been proposed. Most of these are variants of the following recipe: (1) train a binary classifier to predict whether a pair of authors are duplicates, (2) apply the classifier to each pair of ambiguous authors, (3) combine the classification predictions to cluster the records into duplicate sets. Copyright c © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. This approach can be quite accurate, and is attractive because it builds upon existing machine learning technology (e.g., classification and clustering). However, because the core of this approach is a classifier over record pairs, nowhere are aggregate features of an author modeled explicitly. That is, by restricting the model representation to evidence over pairs of authors, we cannot leverage evidence available from examining more than two records. For example, we would like to model the fact that authors are generally affiliated with only a few institutions, have only one or two different email addresses, and are unlikely to publish more than thirty publications in one year. None of these constraints can be captured with a pairwise classifier, which, for example, can only consider whether pairs of institutions or emails match. In this paper we propose a representation for author disambiguation that enables these aggregate constraints. The representation can be understood as a scoring function over a set of authors, indicating how likely it is that all members of the set are duplicates. While flexible, this new representation can make it difficult to estimate the model parameters from training data. We therefore propose a class of training algorithms to estimate the parameters of models adopting this representation. The method has two main characteristics essential to its performance. First, it is errordriven in that training examples are generated based on mistakes made by the prediction algorithm. This approach focuses training effort on the types of examples expected during prediction. Second, it is rank-based in that the loss function induces a ranking over candidate predictions. By representing the difference between predictions, preferences can be expressed over partially-correct or incomplete solutions; additionally, intractable normalization constants can be avoided because the loss function is a ratio of terms. In the following sections, we describe the representation in more detail, then present our proposed training methods. We evaluate our proposals on three real-world author deduplication datasets and demonstrate errorreductions of up to 60%. Author Title Institution Year Y. Li Understanding Social Networks Stanford 2003 Y. Li Understanding Network Protocols Carnegie Mellon 2002 Y. Li Virtual Network Protocols Peking Univ. 2001 Table 1: Author disambiguation example with multiple institutions. Author Co-authors Title P. Cohen A. Howe How evaluation guides AI research P. Cohen M. Greenberg, A. Howe, . Trial by Fire: Understanding the design requirements . in complex environments P. Cohen M. Greenberg MU: a development environment for prospective reasoning systems Table 2: Author disambiguation example with overlapping co-authors.