Hybrid deep pairwise classification for author name disambiguation

Kunho Kim, Shaurya Rohatgi, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2369-2372
Number of pages4
ISBN (Electronic)9781450369763
DOIs
StatePublished - Nov 3 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: Nov 3 2019Nov 7 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period11/3/1911/7/19

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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