Author Name Disambiguation by Using Deep Neural Network

TL;DR

This paper introduces a deep neural network approach for author name disambiguation that automatically learns features, outperforming traditional methods with predefined features, achieving 99.31% accuracy on Vietnamese author data.

Contribution

The paper presents a novel deep learning framework and system architecture for author name disambiguation applicable to any dataset, improving accuracy over existing methods.

Findings

Achieves 99.31% accuracy on Vietnamese author dataset.

Reduces prediction error rate from 1.83% to 0.69%.

Outperforms methods using predefined features.

Abstract

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).