@InProceedings{li-nguyen-ananiadou:2017:BioNLP17,
  author    = {Li, Maolin  and  Nguyen, Nhung  and  Ananiadou, Sophia},
  title     = {Proactive Learning for Named Entity Recognition},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {117--125},
  abstract  = {The goal of active learning is to minimise the cost of producing an annotated
	dataset, in which annotators are assumed to be perfect, i.e., they always
	choose the correct labels. However, in practice, annotators are not infallible,
	and they are likely to assign incorrect labels to some instances. Proactive
	learning is a generalisation of active learning that can model different kinds
	of annotators. Although proactive learning has been applied to certain
	labelling tasks, such as text classification, there is little work on its
	application to named entity (NE) tagging. In this paper, we propose a proactive
	learning method for producing NE annotated corpora, using two annotators with
	different levels of expertise, and who charge different amounts based on their
	levels of experience. To optimise both cost and annotation quality, we also
	propose a mechanism to present multiple sentences to annotators at each
	iteration. Experimental results for several corpora show that our method
	facilitates the construction of high-quality NE labelled datasets at minimal
	cost.},
  url       = {http://www.aclweb.org/anthology/W17-2314}
}