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Two supervised learning approaches for name disambiguation in author citations

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5 Author(s)
H. Han ; Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA ; L. Giles ; H. Zha ; C. Li
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Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases.

Published in:

Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference on

Date of Conference:

7-11 June 2004