  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).
