CiteSeer: an automatic citation indexing system
Proceedings of the third ACM conference on Digital libraries
Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Comparative study of name disambiguation problem using a scalable blocking-based framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
A hierarchical naive Bayes mixture model for name disambiguation in author citations
Proceedings of the 2005 ACM symposium on Applied computing
Effective and scalable solutions for mixed and split citation problems in digital libraries
Proceedings of the 2nd international workshop on Information quality in information systems
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The evaluation of their research work and its effect has always been one of scholars' greatest concerns. The use of citations for that purpose, as proposed by Eugene Garfield, is nowadays widely accepted as the most reliable method. However, gathering a scholar's citations constitutes a particularly laborious task, even in the current Internet era, as one needs to correctly combine information from miscellaneous sources. There exists therefore a need for automating this process. Numerous academic search engines try to cover this need, but none of them addresses successfully all related problems. In this paper we present an approach that facilitates to a great extent citation analysis by taking advantage of new algorithms to deal with these problems.