Disambiguating Geographic Names in a Historical Digital Library
ECDL '01 Proceedings of the 5th European Conference on Research and Advanced Technology for Digital Libraries
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
New Techniques for Disambiguation in Natural Language and Their Application to Biological Text
The Journal of Machine Learning Research
Authorship verification as a one-class classification problem
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
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
Adaptive Name Matching in Information Integration
IEEE Intelligent Systems
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Members of the academic community have increasingly turned to digital libraries to search for the latest work of their peers. On account of their role in the academic community, it is very important that these digital libraries collect citations in a consistent, accurate, and up-to-date manner, yet they do not correctly compile citations for myriads of authors for various reasons including authors with the same name, a problem known as the "name ambiguity problem."This problem occurs when multiple authors share the same name and particularly when names are simplified as in cases where names merely contain the first initial and the last name. In this paper, we propose an automatic algorithm to disambiguate authors in citations by using their specific attribute on the Web. A binary classifier and a cluster separator are applied to cluster citations. Our experiment results show that the disambiguation accuracy can be improved from 51% to 73% when using Web information.