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| A Model of Author Name Disambiguation Based on the Strategy of Targeting Precision before Recall |
| Shen Zhe1, Wang Yi1, Ju Xiufang2, Cheng Ying1 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.Institute for Chinese Social Sciences Research and Assessment, Nanjing University, Nanjing 210093 |
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Abstract Collecting the complete and accurate academic output of each scholar provides the fundamental data needed for bibliometrics and scientific evaluation research. Since the existing author name disambiguation (AND) techniques have not met the demand of practical application, this paper proposes a two-step AND model based on rules for high-level scientific talents that takes full advantage of a rule-based model with high precision and adopts a strategy of targeting precision before recall. Since more features were used due to the feasibility of collecting external data of high-level researchers that contain resumes, representative work, and research interests, the proposed method showed excellent performance. The method was tested with data from the National Science Fund for Distinguished Young Scholars. The experimental results showed that the proposed method performed well both in precision and recall. The F1 score was 0.93 and 0.95 based on two feature sets that were obviously better than the baseline model.
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Received: 08 March 2021
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