Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science

This book introduces empirical methods for machine learning with a special focus on applications in natural language processing (NLP) and data science. The authors present problems of validity, reliability, and significance and provide common solutions based on statistical methodology to solve them. The book focuses on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood famili...