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Explore methods for objectively evaluating manual annotations in machine learning experiments during this 37-minute conference talk from #HLTCon 2018. Learn how to assess the quality and reliability of annotated data used for developing training datasets or creating evaluation datasets for supervised and unsupervised machine learning applications. Gain insights into measuring inter-annotator agreement and building confidence in your gold standard annotations to ensure the success of your machine learning projects.
