@inproceedings{jaschke2013attribute,
  abstract = {We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.},
  author = {Jäschke, Robert and Rudolph, Sebastian},
  booktitle = {Contributions to the 11th International Conference on Formal Concept Analysis},
  editor = {Cellier, Peggy and Distel, Felix and Ganter, Bernhard},
  interhash = {000ab7b0ae3ecd1d7d6ceb39de5c11d4},
  intrahash = {45e900e280661d775d8da949baee3747},
  month = may,
  organization = {Technische Universität Dresden},
  pages = {19--34},
  title = {Attribute Exploration on the Web},
  url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133},
  urn = {urn:nbn:de:bsz:14-qucosa-113133},
  year = 2013
}

@article{raykar2010learning,
  abstract = {For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple experts or annotators. In practice, there is a substantial amount of disagreement among the annotators, and hence it is of great practical interest to address conventional supervised learning problems in this scenario. In this paper we describe a probabilistic approach for supervised learning when we have multiple annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.},
  acmid = {1859894},
  author = {Raykar, Vikas C. and Yu, Shipeng and Zhao, Linda H. and Valadez, Gerardo Hermosillo and Florin, Charles and Bogoni, Luca and Moy, Linda},
  interhash = {8113daf47997fddf48e4c6c79f2eba56},
  intrahash = {14220abe8babfab01c0cdd5ebd5e4b7c},
  issn = {1532-4435},
  issue_date = {3/1/2010},
  journal = {Journal of Machine Learning Research},
  month = aug,
  numpages = {26},
  pages = {1297--1322},
  publisher = {JMLR.org},
  title = {Learning From Crowds},
  url = {http://dl.acm.org/citation.cfm?id=1756006.1859894},
  volume = 11,
  year = 2010
}

@article{lofi2012information,
  abstract = {Recent years brought tremendous advancements in the area of automated information extraction. But still, problem scenarios remain where even state-of-the-art algorithms do not provide a satisfying solution. In these cases, another aspiring recent trend can be exploited to achieve the required extraction quality: explicit crowdsourcing of human intelligence tasks. In this paper, we discuss the synergies between information extraction and crowdsourcing. In particular, we methodically identify and classify the challenges and fallacies that arise when combining both approaches. Furthermore, we argue that for harnessing the full potential of either approach, true hybrid techniques must be considered. To demonstrate this point, we showcase such a hybrid technique, which tightly interweaves information extraction with crowdsourcing and machine learning to vastly surpass the abilities of either technique.},
  address = {Berlin/Heidelberg},
  affiliation = {Institut für Informationssysteme, Technische Universität Braunschweig, Braunschweig, Germany},
  author = {Lofi, Christoph and Selke, Joachim and Balke, Wolf-Tilo},
  doi = {10.1007/s13222-012-0092-8},
  interhash = {941feeaa7bb134e0a5f8b5c0225756b8},
  intrahash = {37cc8f1d19105a073544d6594fbbc033},
  issn = {1618-2162},
  journal = {Datenbank-Spektrum},
  keyword = {Computer Science},
  number = 2,
  pages = {109--120},
  publisher = {Springer},
  title = {Information Extraction Meets Crowdsourcing: A Promising Couple},
  url = {http://dx.doi.org/10.1007/s13222-012-0092-8},
  volume = 12,
  year = 2012
}