@conference {15895,
	title = {Selecting hierarchical clustering cut points for web person-name disambiguation},
	booktitle = {Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval},
	series = {SIGIR {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {778 - 779},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Hierarchical clustering is often used to cluster person-names referring to the same entities. Since the correct number of clusters for a given person-name is not known a priori, some way of deciding where to cut the resulting dendrogram to balance risks of over- or under-clustering is needed. This paper reports on experiments in which outcome-specific and result-set measures are used to learn a global similarity threshold. Results on the Web People Search (WePS)-2 task indicate that approximately 85\% of the optimal F1 measure can be achieved on held-out data.},
	keywords = {clustering, person-name disambiguation},
	isbn = {978-1-60558-483-6},
	doi = {10.1145/1571941.1572124},
	url = {http://doi.acm.org/10.1145/1571941.1572124},
	author = {Gong,Jun and Oard, Douglas}
}
