Real-World Natural Language Processing

Natural Language Processing (NLP) is a branch of AI that focuses on understanding and generating human text and speech. This book teaches how to create practical NLP applications using Python and open-source libraries such as AllenNLP and Fairseq. It covers design, development, and deployment of basic NLP applications, as well as advanced concepts such as attention and transfer learning. The author has worked at Google, Microsoft Research, and Baidu Japan and now runs his own consultancy business.

Format: Paperback / softback
Length: 500 pages
Publication date: 12 January 2022
Publisher: Manning Publications

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.

Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you'll explore the core tools and techniques required to build a huge range of powerful NLP apps.

About the technology:
Natural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines.

About the book:
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. In this practical guide, 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.

What's inside:
Design, develop, and deploy basic NLP applications
NLP libraries such as AllenNLP and Fairseq
Advanced NLP concepts such as attention and transfer learning

About the reader:
Aimed at intermediate Python programmers. No mathematical or machine learning background required.

Weight: 618g
Dimension: 187 x 234 x 24 (mm)
ISBN-13: 9781617296420