# Author Name Disambiguation by Using Deep Neural Network

arXiv: https://arxiv.org/abs/1502.08030 · HTML: https://mlsanity.com/paper/1502.08030/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2015.

## Built on by (most cited first, 4 of 4)

- [A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals](https://mlsanity.com/paper/2201.09555/) (2022)
- [Whois? Deep Author Name Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2207.04772/) (2022)
- [Deep Author Name Disambiguation using DBLP Data](https://mlsanity.com/paper/2303.10067/) (2023)
- [Knowledge-based Refinement of Scientific Publication Knowledge Graphs](https://mlsanity.com/paper/2309.05681/) (2023)

## Abstract (arXiv)

Author name ambiguity decreases the quality and reliability of information retrieved from digital libraries. Existing methods have tried to solve this problem by predefining a feature set based on expert's knowledge for a specific dataset. In this paper, we propose a new approach which uses deep neural network to learn features automatically from data. Additionally, we propose the general system architecture for author name disambiguation on any dataset. In this research, we evaluate the proposed method on a dataset containing Vietnamese author names. The results show that this method significantly outperforms other methods that use predefined feature set. The proposed method achieves 99.31% in terms of accuracy. Prediction error rate decreases from 1.83% to 0.69%, i.e., it decreases by 1.14%, or 62.3% relatively compared with other methods that use predefined feature set (Table 3).