Two supervised learning approaches for name disambiguation in author citations

Hui Han, Lee Giles, Hongyuan Zha, Cheng Li, Kostas Tsioutsiouliklis
2004 Proceedings of the 2004 joint ACM/IEEE conference on Digital libraries - JCDL '04  
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. This paper investigates two supervised learning approaches to disambiguate authors in the citations 1 . One approach uses the naive Bayes
more » ... ty model, a generative model; the other uses Support Vector Machines(SVMs) [39] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: co-author 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.
doi:10.1145/996350.996419 dblp:conf/jcdl/HanGZLT04 fatcat:uwphleq5svc3la6ssi2lpkknyu