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| Author Name Disambiguation with Network Embedding |
Yu Chuanming1( ),Zhong Yunci1,Lin Aochen1,An Lu2 |
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2School of Information Management, Wuhan University, Wuhan 430072, China |
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Abstract [Objective] The paper tries to eliminate the ambiguity of author names in the document system, aiming to solve the problem of incorrect document aggregation.[Methods] First, we constructed three types of networks for authors, documents and author-documents, with structured document data. Then we combined different network embedding methods to obtain the representation of document nodes. Finally, we employed the unsupervised learning model and the hierarchical agglomerative clustering to process the documents.[Results] We conducted empirical studies on datasets from ArnetMiner, CiteSeerX and DBLP. Our method performed well on sparse networks and the macro-F1 value increased by 6%.[Limitations] We only explored author name disambiguation in English.[Conclusions] The proposed method could effectively reduce the ambiguity of author names. It is of great significance for scientific collaboration and citation recommendation, as well as knowledge network related research.
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Received: 11 June 2019
Published: 26 April 2020
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Corresponding Authors:
Yu Chuanming
E-mail: yuchuanming2003@126.com
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