@InProceedings{felt-ringger-seppi:2016:COLING,
  author    = {Felt, Paul  and  Ringger, Eric  and  Seppi, Kevin},
  title     = {Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1787--1796},
  abstract  = {In modern text annotation projects, crowdsourced annotations are 
	often aggregated using item response models 
	or by majority vote. 
	Recently, item response models enhanced with generative 
	data models have been shown to yield substantial benefits 
	over those with conditional or no data models. 
	However, suitable generative data models do not exist for 
	many tasks, such as semantic labeling tasks. 
	When no generative data model exists, 
	we demonstrate that similar benefits may 
	be derived by conditionally modeling documents that have 
	been previously embedded in a semantic space using recent work in 
	vector space models. 
	We use this approach to show state-of-the-art results on a variety of 
	semantic annotation aggregation tasks.},
  url       = {http://aclweb.org/anthology/C16-1168}
}