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False Positive and Cross-relation Signals in Distant Supervision Data.

, , and . AKBC@NIPS, OpenReview.net, (2017)

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CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreement., , , , and . CoRR, (2018)"Dr. Detective": combining gamification techniques and crowdsourcing to create a gold standard in medical text., , , , and . CrowdSem, volume 1030 of CEUR Workshop Proceedings, page 16-31. CEUR-WS.org, (2013)Achieving Expert-Level Annotation Quality with CrowdTruth: The Case of Medical Relation Extraction., , and . BDM2I@ISWC, volume 1428 of CEUR Workshop Proceedings, CEUR-WS.org, (2015)Crowdsourcing Ground Truth for Medical Relation Extraction., , and . ACM Trans. Interact. Intell. Syst., 8 (2): 11:1-11:20 (2018)False Positive and Cross-relation Signals in Distant Supervision Data., , and . AKBC@NIPS, OpenReview.net, (2017)SemEval-2021 Task 12: Learning with Disagreements., , , , , , , and . SemEval@ACL/IJCNLP, page 338-347. Association for Computational Linguistics, (2021)Crowdsourcing Inclusivity: Dealing with Diversity of Opinions, Perspectives and Ambiguity in Annotated Data., , , , , and . WWW (Companion Volume), page 1294-1295. ACM, (2019)Crowdsourcing Ground Truth for Medical Relation Extraction., , and . CoRR, (2017)Crowdsourcing Semantic Label Propagation in Relation Classification., , and . CoRR, (2018)CrowdTruth: Machine-Human Computation Framework for Harnessing Disagreement in Gathering Annotated Data., , , , , , , , and . ISWC (2), volume 8797 of Lecture Notes in Computer Science, page 486-504. Springer, (2014)