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....
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| Main Authors: | , |
|---|---|
| Format: | Book/Monograph |
| Language: | English |
| Published: |
Cham
Springer
[2024]
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| Edition: | Second edition |
| Series: | Synthesis Lectures on Human Language Technologies
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| Online Access: | Verlag, Cover: https://www.dietmardreier.de/annot/564C42696D677C7C393738333033313537303634337C7C434F50.jpg?sq=2
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| Author Notes: | Stefan Riezler, Michael Hagmann |
Table of Contents:
- Preface.- Acknowledgments.- Introduction.- Validity.- Reliability.- Significance.- Worked-Through Example: Analyzing Inferential Reproducibility.- Bibliography.