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

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

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by Aron Culotta , Pallika Kanani , Robert Hall , Michael Wick , Andrew Mccallum

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19 - 7 self

BibTeX

@MISC{Culotta_authordisambiguation,
    author = {Aron Culotta and Pallika Kanani and Robert Hall and Michael Wick and Andrew Mccallum},
    title = {Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function},
    year = {}
}

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Abstract

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.

Keyphrases

author disambiguation ranking loss function error-driven machine learning author disambiguation datasets training data standard binary classification approach clustering step publication database refer first-order feature common supervised machine powerful evidence candidate prediction incorrect prediction error reduction training example

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