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Aggregating and Predicting Sequence Labels from Crowd Annotations.

, , , , and . ACL (1), page 299-309. Association for Computational Linguistics, (2017)

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Incorporating Relevance and Pseudo-relevance Feedback in the Markov Random Field Model.. TREC, volume 500-277 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2008)Overview of the TREC 2013 Crowdsourcing Track., , and . TREC, volume 500-302 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2013)Overview of the TREC 2012 Crowdsourcing Track., , and . TREC, volume 500-298 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2012)Improving Quality of Crowdsourced Labels via Probabilistic Matrix Factorization., and . HCOMP@AAAI, volume WS-12-08 of AAAI Technical Report, AAAI Press, (2012)Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection., , , and . WWW, page 3689-3700. ACM, (2023)Learning a neural Pareto manifold extractor with constraints., , , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 749-758. PMLR, (2022)Aggregating Complex Annotations via Merging and Matching., and . KDD, page 86-94. ACM, (2021)Linguistic Resources for Speech Parsing., , , , , , , and . LREC, page 629-634. European Language Resources Association (ELRA), (2006)An improved markov random field model for supporting verbose queries.. SIGIR, page 476-483. ACM, (2009)Crowd vs. Expert: What Can Relevance Judgment Rationales Teach Us About Assessor Disagreement?, , , , and . SIGIR, page 805-814. ACM, (2018)