NDLI: Combining machine learning and human judgment in author disambiguation

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Combining machine learning and human judgment in author disambiguation

Content Provider	ACM Digital Library
Author	Qian, Yanan Cui, Jianling Zheng, Qinghua Nie, Zaiqing Hu, Yunhua
Abstract	Author disambiguation in digital libraries becomes increasingly difficult as the number of publications and consequently the number of ambiguous author names keep growing. The fully automatic author disambiguation approach could not give satisfactory results due to the lack of signals in many cases. Furthermore, human judgment on the basis of automatic algorithms is also not suitable because the automatically disambiguated results are often mixed and not understandable for humans. In this paper, we propose a Labeling Oriented Author Disambiguation approach, called LOAD, to combine machine learning and human judgment together in author disambiguation. LOAD exploits a framework which consists of high precision clustering, high recall clustering, and top dissimilar clusters selection and ranking. In the framework, supervised learning algorithms are used to train the similarity functions between publications and a clustering algorithm is further applied to generate clusters. To validate the effectiveness and efficiency of the proposed LOAD approach, comprehensive experiments are conducted. Comparing to conventional author disambiguation algorithms, the LOAD yields much more accurate results to assist human labeling. Further experiments show that the LOAD approach can save labeling time dramatically.
Starting Page	1241
Ending Page	1246
Page Count	6
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2063756
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User contribution Author disambiguation Human judgment
Content Type	Text
Resource Type	Article

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