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This lecture covers the NLP evaluation protocol, creation of gold standards, inter-annotator agreement, Cohen’s kappa, precision, recall, and statistical significance. It discusses the importance of separating data into training, validation, and test sets, and the use of evaluation metrics like confusion matrix, F-score, and more.