Natural Language Processing with Spark NLP: Learning to Understand Text at Scale

October 31, 2020
Natural Language Processing with Spark NLP: Learning to Understand Text at Scale

If you want to build an enterprise-quality application that usesnatural language text but aren't sure where to begin or what toolsto use, this practical guide will help get you started. AlexThomas, principal data scientist at Wisecube, shows softwareengineers and data scientists how to build scalable naturallanguage processing (NLP) applications using deep learning and theApache Spark NLP library.Through concrete examples, practical and theoreticalexplanations, and hands-on exercises for using NLP on the Sparkprocessing framework, this book teaches you everything from basiclinguistics and writing systems to sentiment analysis and searchengines. You'll also explore special concerns for developingtext-based applications, such as performance.In four sections, you'll learn NLP basics and building blocksbefore diving into application and system building:Basics: Understand the fundamentals of naturallanguage processing, NLP on Apache Stark, and deep learningBuilding blocks: Learn techniques for buildingNLP applications—including tokenization, sentence segmentation, andnamed-entity recognition—and discover how and why they workApplications: Explore the design, development,and experimentation process for building your own NLPapplicationsBuilding NLP systems: Consider options forproductionizing and deploying NLP models, including which humanlanguages to support