1st Edition
Teaching Computers to Read Effective Best Practices in Building Valuable NLP Solutions
Acronyms and Definitions
Preface
Acknowledgments
1. Debunking Common Myths in Natural Language Processing
2. The Trajectory of Natural Language Processing: Classic, Modern, and Generative
3. Large Language Models and Generative Artificial Intelligence
4. Pre-processing and Exploratory Data Analysis for NLP
5. Framing the Task and Data Labeling
6. Data Curation for NLP Corpora
7. Machine Learning Approaches for Natural Language Problems
8. Working Across Languages in NLP
9. Evaluating Performance of NLP Solutions
10. Maintaining Value: Deploying and Monitoring NLP Solutions
11. NLPOps: The Mechanics of NLP Production at Scale
12. Ethics in Data Science and NLP
13. Key Factors for Successful NLP Solutions
Index
Biography
Rachel Wagner-Kaiser has 15 years of experience in data and AI, entering the data science field after completing her PhD in astronomy. She specializes in building NLP solutions for real-world problems constrained by limited or messy data. Rachel leads technical teams to design, build, deploy, and maintain NLP solutions, and her expertise has helped companies organize and decode their unstructured data to solve a variety of business problems and drive value through automation.
