If you want to build an enterprise-quality application that uses natural
language text but aren't sure where to begin or what tools to use, this
practical guide will help get you started. Alex Thomas, principal data
scientist at Wisecube, shows software engineers and data scientists how
to build scalable natural language processing (NLP) applications using
deep learning and the Apache Spark NLP library.
Through concrete examples, practical and theoretical explanations, and
hands-on exercises for using NLP on the Spark processing framework, this
book teaches you everything from basic linguistics and writing systems
to sentiment analysis and search engines. You'll also explore special
concerns for developing text-based applications, such as performance.
In four sections, you'll learn NLP basics and building blocks before
diving into application and system building:
Basics: Understand the fundamentals of natural language processing,
NLP on Apache Stark, and deep learning
Building blocks: Learn techniques for building NLP
applications-including tokenization, sentence segmentation, and
named-entity recognition-and discover how and why they work
Applications: Explore the design, development, and experimentation
process for building your own NLP applications
Building NLP systems: Consider options for productionizing and
deploying NLP models, including which human languages to support
