Real-World Natural Language Processing: Practical applications with deep learning

Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq—without getting bogged down in complex language theory and the mathematics of deep learning.

Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. In Real-World Natural Language Processing, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps.

Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq—without getting bogged down in complex language theory and the mathematics of deep learning. You’ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you’ll use in all different kinds of NLP programs. By the time you’re done, you’ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems.