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Information Extraction Meets Crowdsourcing: A Promising Couple

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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.

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Notes

  1. For more detailed information, see http://crowdflower.com/docs/gold.

  2. http://samasource.org/.

  3. http://www.facebook.com/press/info.php?statistics.

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Authors and Affiliations

  1. Institut für Informationssysteme, Technische Universität Braunschweig, Braunschweig, Germany

    Christoph Lofi, Joachim Selke & Wolf-Tilo Balke

Authors
  1. Christoph Lofi
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  2. Joachim Selke
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  3. Wolf-Tilo Balke
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Correspondence to Christoph Lofi.

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Lofi, C., Selke, J. & Balke, WT. Information Extraction Meets Crowdsourcing: A Promising Couple. Datenbank Spektrum 12, 109–120 (2012). https://doi.org/10.1007/s13222-012-0092-8

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