Author Name Disambiguation Using a New Categorical Distribution Similarity

Author name ambiguity has been a long-standing problem which impairs the accuracy of publication retrieval and bibliometric methods. Most of the existing disambiguation methods are built on similarity measures, e.g., “Jaccard Coefficient”, between two sets of papers to be disambiguated, each set represented by a set of categorical features, e.g., coauthors and published venues. Such measures perform bad when the two sets are small, which is typical in Author Name Disambiguation. In this paper, we propose a novel categorical set similarity measure. We model an author’s preference, e.g., to venues, using a categorical distribution, and derive a likelihood ratio to estimate the likelihood that the two sets are drawn from the same distribution. This likelihood ratio is used as the similarity measure to decide whether two sets belong to the same author. This measure is mathematically principled and verified to perform well even when the cardinalities of the two compared sets are small. Additionally, we propose a new method to estimate the number of distinct authors for a given name based on the name statistics extracted from a digital library. Experiment shows that our method significantly outperforms a baseline method, a widely used benchmark method, and a real system.