Disambiguating Author Names with Embedding Heterogeneous Information and Attentive RNN Clustering Parameters
Wang Ruolin1,Niu Zhendong1,2(),Lin Qika3,Zhu Yifan1,Qiu Ping1,Lu Hao4,Liu Donglei1
1School of Computer, Beijing Institute of Technology, Beijing 100081, China 2Beijing Institute of Technology Library, Beijing 100081, China 3School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China 4Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
[Objective] This paper proposes a name disambiguation method for scientific literature, aiming to distinguish scholars with the same name. The existing solutions utilizes document feature extraction or relationship between documents and co-authors, which loses higher-order attributes. [Methods] First, we established a unified feature extraction framework of Paper Embedding Network (PaperEmbNet), which combined content and relationship to build an academic heterogeneous information network for each author. Then, we designed a Clustering Parameters Method (AR4CPM) based on the Attentive Recurrent Neural Network to estimate the clustering number directly. Finally, we used the Hierarchical agglomerative clustering algorithm (HAC) to disambiguate author names with the predicted number as the preset parameter. [Results] We examined the proposed model with the AMiner-AND dataset and found the macro-F1 score was up to 4.75% higher than the suboptimal model, and the average training time was 5-10 minutes shorter than the existing baselines. [Limitations] We need to evaluate the performance of the proposed method with multilingual environment. [Conclusions] The proposed approach could effectively conduct the name disambiguation tasks.
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