Co-attention-Based Pairwise Learning for Author Name Disambiguation

Shenghui Wang*, Qiuke Li, Rob Koopman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Digital libraries face a pressing issue of author name ambiguity. This paper proposes a novel pairwise learning model for author name disambiguation, utilizing self-attention and co-attention mechanisms. The model integrates textual, discrete, and co-author attributes, amongst others, to capture comprehensive information from bibliographic records. It incorporates an optional random projection-based dimension reduction technique for efficiency to handle large datasets. The attention weight visualizations provide explanations for the model’s predictions. Our experiments on a substantial bibliographic catalogue repository validate the model’s effectiveness using accuracy, F1, and ROC AUC scores.

Original languageEnglish
Title of host publicationLeveraging Generative Intelligence in Digital Libraries
Subtitle of host publicationTowards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings
EditorsDion H. Goh, Shu-Jiun Chen, Suppawong Tuarob
Number of pages10
ISBN (Electronic)978-981-99-8088-8
ISBN (Print)978-981-99-8087-1
Publication statusPublished - 30 Nov 2023
Event25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023 - Taipei, Taiwan
Duration: 4 Dec 20237 Dec 2023
Conference number: 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023
Abbreviated titleICADL 2023


  • Attention mechanisms
  • Author name disambiguation
  • Explainable machine learning
  • Feature Integration
  • 2024 OA procedure


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