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| Auto-Identification of Authors Affiliation Based on Class-Center Vectors |
| He Tao, Wang Guifang, and Ma |
| Wuhan Documentation and Information Center, Chinese Academy of Sciences, Wuhan 430071 |
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Abstract When analyzing a large amount of scientific and technical literature, identification of the author s affiliation is always necessary. A key step in this task is matching the author s address to the corresponding institution. Authors from one institution often state their affiliations in various forms in English. This causes string-matching methods to yield unsatisfactory results. In this paper, a machine learning method known as “class-center vectors” has been proposed to solve this problem according to the characteristics of the author s address. Compared with traditional methods, our method does not require matching rules to be written manually. The experimental results of Chinese Academy of Sciences (CAS) author s address data sets illustrate the feasibility of our method.
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Received: 13 August 2018
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