Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations

MLA

Russell Richie, et al. “Inter-Annotator Agreement Is Not the Ceiling of Machine Learning Performance: Evidence from a Comprehensive Set of Simulations.” Proceedings of the 21st Workshop on Biomedical Language Processing, Jan. 2022. EBSCOhost, https://doi.org/10.18653/v1/2022.bionlp-1.26.

APA

Russell Richie, Sachin Grover, & Fuchiang (Rich) Tsui. (2022). Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations. Proceedings of the 21st Workshop on Biomedical Language Processing. https://doi.org/10.18653/v1/2022.bionlp-1.26

Chicago

Russell Richie, Sachin Grover, and Fuchiang (Rich) Tsui. 2022. “Inter-Annotator Agreement Is Not the Ceiling of Machine Learning Performance: Evidence from a Comprehensive Set of Simulations.” Proceedings of the 21st Workshop on Biomedical Language Processing, January. doi:10.18653/v1/2022.bionlp-1.26.