Confidence-based stopping criteria for active learning for data annotation

doi:10.1145/1753783.1753784

Confidence-based stopping criteria for active learning for data annotation

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Authors:	Jingbo Zhu	Northeastern University, China
	Huizhen Wang	Northeastern University, China
	Eduard Hovy	University of Southern California, Marina del Rey, CA
	Matthew Ma	Scientific Works, Princeton Junction, NJ

	2010 Article Research Refereed
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	ACM Transactions on Speech and Language Processing (TSLP) TSLP Homepage archive
	Volume 6 Issue 3, April 2010 ACM New York, NY, USA table of contents doi>10.1145/1753783.1753784

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Tags: active learning algorithms confidence estimation experimentation languages learning natural language processing stopping criterion text classification theory uncertainty sampling word sense disambiguation

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