Book Description: Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science Stefan Riezler June 1, 2022 Pages: Genre: Non-Fiction, Technology, Machine Learning, Data Science Synopsis: In this book, [Insert Author's Name] delves into the methodological questions of empirical sciences, specifically in the fields of natural language processing (NLP) and data science, addressing issues of validity, reliability, and significance through statistical techniques. The author focuses on model-based empirical methods that utilize interpretable probabilistic models from well-understood families such as generalized additive models (GAMs) and linear mixed effects models (LMEMs). These models are used to assess the validity, reliability, and significance of data annotation and machine learning predictions. The book begins by discussing the need to study and understand the technological process of developing modern knowledge, emphasizing the importance of developing a personal paradigm for perceiving the technological evolution of humanity. This paradigm is crucial for the survival of humanity and the unification of people in a warring state. The author argues that understanding these concepts is essential for the future of humanity and the ability to adapt to new technologies. The book is divided into three main sections, each focusing on a specific aspect of empirical methods. The first section covers the validity test, which detects circular features that circumvent learning.

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