@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{liu2012crowdsourcing,
  abstract = {Some complex problems, such as image tagging and natural language processing, are very challenging for computers, where even state-of-the-art technology is yet able to provide satisfactory accuracy. Therefore, rather than relying solely on developing new and better algorithms to handle such tasks, we look to the crowdsourcing solution -- employing human participation -- to make good the shortfall in current technology. Crowdsourcing is a good supplement to many computer tasks. A complex job may be divided into computer-oriented tasks and human-oriented tasks, which are then assigned to machines and humans respectively.</p> <p>To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality.},
  acmid = {2336676},
  author = {Liu, Xuan and Lu, Meiyu and Ooi, Beng Chin and Shen, Yanyan and Wu, Sai and Zhang, Meihui},
  interhash = {41ad6e73b03373d76d3164ba248335d7},
  intrahash = {2091967734f96c4afbc09319d48a8c65},
  issn = {2150-8097},
  issue_date = {June 2012},
  journal = {Proceedings of the VLDB Endowment},
  month = jun,
  number = 10,
  numpages = {12},
  pages = {1040--1051},
  publisher = {VLDB Endowment},
  title = {CDAS: a crowdsourcing data analytics system},
  url = {http://dl.acm.org/citation.cfm?id=2336664.2336676},
  volume = 5,
  year = 2012
}

@inproceedings{brew2010using,
  abstract = {Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsource Crowdsourcing is a term, sometimes associated with Web 2.0 technologies, that describes outsourcing of tasks to a large often anonymous community. much of the annotation work that is required to train a machine learning system to perform sentiment scoring. We describe such a system for tracking economic sentiment in online media that has been deployed since August 2009. It uses annotations provided by a cohort of non-expert annotators to train a learning system to classify a large body of news items. We report on the design challenges addressed in managing the effort of the annotators and in making annotation an interesting experience.},
  acmid = {1860997},
  address = {Amsterdam, The Netherlands, The Netherlands},
  author = {Brew, Anthony and Greene, Derek and Cunningham, Pádraig},
  booktitle = {Proceedings of the 19th European Conference on Artificial Intelligence},
  editor = {Coelho, Helder and Studer, Rudi and Wooldridge, Michael},
  interhash = {90650749ea1084b729710d37b5865b72},
  intrahash = {9643e3c5729886b0b4e85cb3d3d704f5},
  isbn = {978-1-60750-605-8},
  numpages = {6},
  pages = {145--150},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  title = {Using Crowdsourcing and Active Learning to Track Sentiment in Online Media},
  url = {http://dl.acm.org/citation.cfm?id=1860967.1860997},
  volume = 215,
  year = 2010
}