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Crowd vs. Expert: What Can Relevance Judgment Rationales Teach Us About Assessor Disagreement?

, , , , and . SIGIR, page 805-814. ACM, (2018)

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Divide and conquer: neuroevolution for multiclass classification., , , , , , , , and . GECCO, page 474-481. ACM, (2018)Your Behavior Signals Your Reliability: Modeling Crowd Behavioral Traces to Ensure Quality Relevance Annotations., , , , and . HCOMP, page 41-49. AAAI Press, (2018)Surrogate-assisted neuroevolution., and . GECCO, page 1048-1056. ACM, (2022)Neural Information Retrieval: A Literature Review., , , , , , , , , and 5 other author(s). CoRR, (2016)Why Is That Relevant? Collecting Annotator Rationales for Relevance Judgments., , , and . HCOMP, page 139-148. AAAI Press, (2016)A Potts model approach to unsupervised graph clustering with Graph Neural Networks., , and . CoRR, (2023)Annotator Rationales for Labeling Tasks in Crowdsourcing., , , and . J. Artif. Intell. Res., (2020)Mix and Match: Collaborative Expert-Crowd Judging for Building Test Collections Accurately and Affordably., , , , and . DESIRES, volume 2167 of CEUR Workshop Proceedings, page 42-46. CEUR-WS.org, (2018)Neural information retrieval: at the end of the early years., , , , , , , , , and 9 other author(s). Inf. Retr. J., 21 (2-3): 111-182 (2018)An Empirical Study of API Stability and Adoption in the Android Ecosystem., , and . ICSM, page 70-79. IEEE Computer Society, (2013)