Principles of data mining
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
Why inverse document frequency?
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Person resolution in person search results: WebHawk
Proceedings of the 14th ACM international conference on Information and knowledge management
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The SemEval-2007 WePS evaluation: establishing a benchmark for the web people search task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Extracting key phrases to disambiguate personal names on the web
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
The role of named entities in web people search
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
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Most of the previous works that disambiguate personal names in Web search results employ agglomerative clustering approaches. However, these approaches tend to generate clusters that contain a single element depending on a certain criterion of merging similar clusters. In contrast to such previous works, we have adopted a semi-supervised clustering approach to integrate similar documents into a labeled document. Moreover, our proposed approach is characterized by controlling the fluctuation of the centroid of a cluster in order to generate more accurate clusters.