Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques. The methodological questions addressed in this book include the problems of validity, reliability, and significance. In the case of machine learning, these correspond to the questions of whether a model predicts what it purports to predict, whether a model's performance is consistent across replications, and whether a performance difference between two models is due to chance, respectively. The goal of this book is to answer these questions by concrete statistical tests that can be applied to assess validity, reliability, and significance of data annotation and machine learning prediction in the fields of NLP and data science. Our focus is on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs). Based on the interpretable parameters of the trained GAMs or LMEMs, the book presents model-based statistical tests such as a validity test that allows detecting circular features that circumvent learning. Furthermore, the book discusses a reliability coefficient using variance decomposition based on random effect parameters of LMEMs. Last, a significance test based on the likelihood ratio of nested LMEMs trained on the performance scores of two machine learning models is shown to naturally allow the inclusion of variations in meta-parameter settings into hypothesis testing, and further facilitates a refined system comparison conditional on properties of input data. This book can be used as an introduction to empirical methods for machine learning in general, with a special focus on applications in NLP and data science. The book is self-contained, with an appendix on the mathematical background on GAMs and LMEMs, and with an accompanying webpage including R code to replicate experiments presented in the book.

Autorentext

Stefan Riezler is a full professor in the Department of Computational Linguistics at Heidelberg University, Germany since 2010, and also co-opted in Informatics at the Department of Mathematics and Computer Science. He received his Ph.D. (with distinction) in Computational Linguistics from the University of Tübingen in 1998, conducted post-doctoral work at Brown University in 1999, and spent a decade in industry research (Xerox PARC, Google Research). His research focus is on interactive machine learning for natural language processing problems especially for the application areas of cross-lingual information retrieval and statistical machine translation. He is engaged as an editorial board member of the main journals of the field-Computational Linguistics and Transactions of the Association for Computational Linguistics-and is a regular member of the program committee of various natural language processing and machine learning conferences. He has published more than 100 journal and conference papers in these areas. He also conducts interdisciplinary research as member of the Interdisciplinary Center for Scientific Computing (IWR), for example, on the topic of early prediction of sepsis using machine learning and natural language processing techniques.Michael Hagmann is a graduate research assistant in the Department of Computational Linguistics at Heidelberg University, Germany, since 2019. He holds an M.Sc. in Statistics (with distinction) from the University of Vienna, Austria. He received an award for the best Master's thesis in Applied Statistics from the Austrian Statistical Society. He has worked as a medical statistician at the medical faculty of Heidelberg University in Mannheim, Germany and in the section for Medical Statistics at the Medical University of Vienna, Austria. His research focus is on statistical methods for data science and, recently, NLP. He has published more than 50 papers in journals for medical research and mathematical statistics.