Hybrid deep pairwise classification for author name disambiguation

Abstract

Author name disambiguation (AND) can be defined as the problem of clustering together unique authors from all author mentions that have been extracted from publication or related records in digital libraries or other sources. Pairwise classification is an essential part of AND, and is used to estimate the probability that any pair of author mentions belong to the same author. Previous studies trained classifiers with features manually extracted from each attribute of the data. Recently, others trained a model to learn a vector representation from text without considering any structure information. Both of these approaches have advantages. The former method takes advantage of the structure of data, while the latter takes into account the textual similarity across attributes. Here, we introduce a hybrid method which takes advantage of both approaches by extracting both structure-aware features and global features. In addition, we introduce a novel way to train a global model utilizing a large number of negative samples. Results on AMiner and PubMed data shows the relative improvement of the mean average precision (MAP) by more than 7.45% when compared to previous state-of-the-art methods.