# Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data

arXiv: https://arxiv.org/abs/2107.04382 · HTML: https://mlsanity.com/paper/2107.04382/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [A Joint Model for Word Embedding and Word Morphology](https://mlsanity.com/paper/1606.02601/) (2016)
- [Natural Language Processing for Information Extraction](https://mlsanity.com/paper/1807.02383/) (2018)
- [Evaluating Co-Authorship Networks in Author Name Disambiguation for Common Names](https://mlsanity.com/paper/1606.03857/) (2016)
- [BiblioDAP: The 1st Workshop on Bibliographic Data Analysis and Processing](https://mlsanity.com/paper/2106.12320/) (2021)

## Built on by (most cited first, 1 of 1)

- [Recent Developments in Deep Learning-based Author Name Disambiguation](https://mlsanity.com/paper/2503.13448/) (2024)

## Abstract (arXiv)

Author name ambiguity remains a critical open problem in digital libraries due to synonymy and homonymy of names. In this paper, we propose a novel approach to link author names to their real-world entities by relying on their co-authorship pattern and area of research. Our supervised deep learning model identifies an author by capturing his/her relationship with his/her co-authors and area of research, which is represented by the titles and sources of the target author's publications. These attributes are encoded by their semantic and symbolic representations. To this end, Bib2Auth uses ~ 22K bibliographic records from the DBLP repository and is trained with each pair of co-authors. The extensive experiments have proved the capability of the approach to distinguish between authors sharing the same name and recognize authors with different name variations. Bib2Auth has shown good performance on a relatively large dataset, which qualifies it to be directly integrated into bibliographic indices.