# Recent Developments in Deep Learning-based Author Name Disambiguation

arXiv: https://arxiv.org/abs/2503.13448 · HTML: https://mlsanity.com/paper/2503.13448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [OpenCitations, an infrastructure organization for open scholarship](https://mlsanity.com/paper/1906.11964/) (2019)
- [Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks](https://mlsanity.com/paper/2008.13099/) (2020)
- [Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit](https://mlsanity.com/paper/2302.11848/) (2023)
- [A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals](https://mlsanity.com/paper/2201.09555/) (2022)
- [Accuracy of simple, initials-based methods for author name disambiguation](https://mlsanity.com/paper/1308.0749/) (2013)
- [CONNA: Addressing Name Disambiguation on The Fly](https://mlsanity.com/paper/1910.12202/) (2019)
- [Whois? Deep Author Name Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2207.04772/) (2022)
- [Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives](https://mlsanity.com/paper/2212.12715/) (2022)
- [BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting](https://mlsanity.com/paper/2404.08322/) (2024)
- [Effect of forename string on author name disambiguation](https://mlsanity.com/paper/2102.03250/) (2021)
- [Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2107.04382/) (2021)
- [Scholar Name Disambiguation with Search-enhanced LLM Across Language](https://mlsanity.com/paper/2411.17102/) (2024)

## Abstract (arXiv)

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 used by researchers and the presence of intrinsic linguistic challenges, such as homonymy, the development of Deep Learning algorithms to address this issue has become widespread. Many AND deep learning methods have been developed, and surveys exist comparing the approaches in terms of techniques, complexity, performance. However, none explicitly addresses AND methods in the context of deep learning in the latest years (i.e. timeframe 2016-2024). In this paper, we provide a systematic review of state-of-the-art AND techniques based on deep learning, highlighting recent improvements, challenges, and open issues in the field. We find that DL methods have significantly impacted AND by enabling the integration of structured and unstructured data, and hybrid approaches effectively balance supervised and unsupervised learning.