This paper proposes an automatic article author name disambiguation method based on rule matching and machine learning. For each article, the candidate authors are determined based on artificial constructed name matching rules firstly. For the cases of multiple candidates, features are extracted from the attribute information of the article, such as collaborators, title, abstract, key words and publication name, and then selected machine learning models are applied to author name disambiguating. The experimental results show that the K-nearest neighbor and Softmax classifier are more suitable for the author name disambiguation task than other models. In addition, extracting features of the authors information from other information separately can effectively improve the accuracy of the author name disambiguation.
引用本文格式： 邓可君,华凯,邓昌明,姜宁,袁玲,彭一明,张治坤. 基于机器学习的论文作者名消歧方法研究[J]. 四川大学学报: 自然科学版, 2019, 56: 241.复制