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

Author:   Stefan Riezler ,  Michael Hagmann
Publisher:   Springer International Publishing AG
ISBN:  

9783031010552


Pages:   147
Publication Date:   03 December 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science


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Author:   Stefan Riezler ,  Michael Hagmann
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Weight:   0.329kg
ISBN:  

9783031010552


ISBN 10:   3031010558
Pages:   147
Publication Date:   03 December 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.
Language:   English

Table of Contents

Preface.- Acknowledgments.- Introduction.- Validity.- Reliability.- Significance.- Bibliography.- Authors' Biographies.

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Author Information

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.

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