Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations-Reference-Cited by-同舟云学术

Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The World Wide Web Conference on - WWW '19
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Author:

Li Yuan¹,I. P. Rubinstein Benjamin¹,Cohn Trevor¹

Affiliation:

1. The University of Melbourne, Australia

Publisher

ACM Press

Reference29 articles.

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2. Chris Callison-Burch and Mark Dredze. 2010. Creating speech and language data with Amazon's Mechanical Turk. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk. Association for Computational Linguistics, 1-12.

3. Alexander Philip Dawid and Allan M Skene. 1979. Maximum likelihood estimation of observer error-rates using the EM algorithm. Applied Statistics (1979), 20-28.

4. Gianluca Demartini, Djellel Eddine Difallah, and Philippe Cudre´-Mauroux. 2012. ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In Proceedings of the 21st international conference on World Wide Web. ACM, 469-478.

5. Arthur P Dempster, Nan M Laird, and Donald B Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological) (1977), 1-38.

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