FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Improving author coreference by resource-bounded information gathering from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ADANA: Active Name Disambiguation
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Learning to Group Web Text Incorporating Prior Information
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
A Unified Probabilistic Framework for Name Disambiguation in Digital Library
IEEE Transactions on Knowledge and Data Engineering
Active associative sampling for author name disambiguation
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Name disambiguation is a challenging and important problem in many domains, such as digital libraries, social media management and people search systems. Traditional methods, based on direct assignment using supervised machine learning techniques, seem to be the most effective, but their performances are highly dependent on the amount of training data, while large data annotation can be expensive and time-consuming requiring hours of manual inspection by a domain expert. To efficiently acquire labeled data, we propose a bootstrapping algorithm for the name disambiguation task based on active learning and crowdsourced labeling. We show that the proposed method can leverage the advantages of exploration and exploitation by combining two strategies, thereby improving the overall quality of the training data at minimal expense. The experimental results on two datasets DBLP and ArnetMiner demonstrate the superiority of our framework over existing methods.