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This video tutorial focuses on evaluating annotation quality, specifically through inner annotator agreement using Cohen's Kappa. It provides a case example for computing Cohen's Kappa with a small set of annotations and discusses the interpretation of Kappa values. The video also addresses how to adjudicate disagreements to establish a gold standard set of labels. As the final installment in a series on the data collection pipeline, it emphasizes the significance of assessing annotation quality post-collection, highlighting that high-quality annotations enhance the credibility of a dataset for potential users and serve as an indicator of its effectiveness for model training. Viewers are encouraged to watch the previous four videos for context.
