Deep Learning Approaches to Author Name Disambiguation: A Survey

Abstract Purpose: Author Name Disambiguation (AND) is a critical task for digital libraries aiming to link existing authors with their respective publications. Due to the lack of persistent identifiers and intrinsic linguistic challenges such as homonymy, deep learning algorithms have become widespread for addressing this issue. This paper provides a systematic review of state-of-the-art AND techniques based on deep learning within the timeframe 2016-2024, filling a gap in existing literature. Methods: We conducted a systematic literature review using Google Scholar with keywords "author name disambiguation" + "deep learning" and temporal filters. The search yielded 52 documents, of which 28 were selected after full-text assessment. We categorized approaches based on learning paradigms: supervised, unsupervised, and hybrid methods. Additionally, we performed bibliometric analysis using OpenCitations data with network visualization via Gephi. Results: We identified 28 relevant studies employing deep learning for AND. The analysis of the comparable studies reveals that hybrid approaches achieve the best performance, with the top-performing method reaching 89.7 F1-score on AMiner datasets. Supervised methods predominantly focus on author assignment tasks, while unsupervised approaches excel at author grouping. Conclusion: Deep learning methods have significantly impacted AND by enabling integration of structured and unstructured data. Hybrid approaches effectively balance supervised and unsupervised learning, demonstrating superior performance. However, heavy reliance on AMiner datasets and lack of standardized evaluation frameworks remain critical challenges for the field’s advancement.