DataSift: An Expressive and Accurate Crowd-Powered Search Toolkit

Authors

  • Aditya Parameswaran Stanford University
  • Ming Han Teh Stanford University
  • Hector Garcia-Molina Stanford University
  • Jennifer Widom Stanford University

DOI:

https://doi.org/10.1609/hcomp.v1i1.13077

Keywords:

search, sift, crowd, toolkit, system

Abstract

Traditional information retrieval systems have limited functionality. For instance, they are not able to adequately support queries containing non-textual fragments such as images or videos, queries that are very long or ambiguous, or semantically-rich queries over non-textual corpora. In this paper, we present DataSift, an expressive and accurate crowd-powered search toolkit that can connect to any corpus. We provide a number of alternative configurations for DataSift using crowdsourced and automated components, and demonstrate gains of 2–3x on precision over traditional retrieval schemes using experiments on real corpora. We also present our results on determining suitable values for parameters in those configurations, along with a number of interesting insights learned along the way.

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Published

2013-11-03

How to Cite

Parameswaran, A., Teh, M. H., Garcia-Molina, H., & Widom, J. (2013). DataSift: An Expressive and Accurate Crowd-Powered Search Toolkit. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 1(1), 112-120. https://doi.org/10.1609/hcomp.v1i1.13077