10 great books if you want to learn about natural language processing

Natural language processing (NLP) is a major part of artificial intelligence. There is a large body of literature covering the subject. But how do you find the best NLP books?

A simple solution is to ask the experts. That’s why we’ve put together our top ten must-read books (and ebooks) on NLP. We are confident that each one will help you develop or expand your knowledge and ultimately develop your skills in this field.

You are ready: Let’s begin.

10 Must Read Books on NLP

A quick note before we get into the list. Some of these books cover more basic elements of NLP.

So if you already have a background in the topic, this article might be more of a quick review, but in any case, the basics are important for growth, so we’re sure everyone can find value in the resources below.

Author:Joseph D. Dumb

Natural Language Processing Book

The first book on our list focuses on machine learning-based NLP. The concept revolves around software that can recognize patterns using broad context to infer meaning and interpret poorly structured text.

Joseph Booth guides readers through a topic using a simple process that interprets the written text before providing a reasoned response. And his approach empowers practitioners to tackle the more important challenges of NLP development.

You’ll find everything you need to know to build NLP solutions, from an overview of natural language processes to what technology can do. The book covers tasks such as extracting sentences and specific words, labeling, recognizing entities, and answering questions, among lesser-known use cases.

Authors: Sovmya VajalaBodhisattva Majumder, Anuj Gupta, Harshit Surana

Natural Language Processing Book

Several authors collaborated here to introduce readers to how to build real-world NLP solutions embedded in larger product settings.

The book teaches you how to apply NLP in an industrial setting while offering a comprehensive overview of key advances in the field (illustrated using both real-life case studies and code).

Here’s what’s covered.

  • A wide spectrum of problem statements, tasks and solution approaches within NLP
  • How to use machine learning and deep learning in NLP applications
  • How to evaluate algorithms and techniques for NLP product tasks and datasets
  • How to produce software solutions that follow best practices
  • Implementation Best Practices (along with NLP Opportunities in Business)

This book is best suited for software engineers, data scientists, machine learning engineers, product managers, and business leaders.

Authors:Uday KamathJohn Liu, James Whittaker

Natural Language Processing Book

This book explores deep learning architectures in tasks such as document classification, translation, language modeling, and speech recognition.

The authors walk readers through the topic in three parts, each targeting a different subset based on your experience and prior knowledge.

  • Part 1: Introduction to Machine Learning, NLP and Speech

This section introduces readers to speech recognition, deep learning, and machine learning using fundamental theory and case studies.

  • Part 2: Fundamentals of Deep Learning

This section covers basic speech and text processing topics.

  • Part 3: Advanced Techniques for Deep Learning Text and Speech

The final part explains the latest deep learning research at the intersection of NLP and speech, including attention mechanisms, transfer learning, multitasking learning, reinforcement learning, and case studies.

The book will interest everyone. you just need to choose the section that best suits your interests and experience.

Author: Dennis Rothman

NLP books

Now onto the book, which explores in detail deep learning for machine translation, question answering, text-to-speech, speech-to-text, language modeling and other NLP areas with transformers.

The author walks readers through NLP with Python, describing how to use the latest pre-built transformer models (along with various other NLP platforms). He also looks at the use of Python, TensorFlow and Keras frameworks for sentiment analysis, text summarization, speech recognition, machine translation and much more.

So if you have some familiarity with neural networks, Python, PyTorch, or TensorFlow and want to learn more about transformers, this book is for you.

Author: Jacob Eisenstein

Natural Language Processing Book

This book provides a perspective on NLP techniques for developing models that can understand, generate, and manipulate human language. The author covers state-of-the-art data-driven approaches with a focus on supervised and unsupervised machine learning techniques.

The book is divided into four parts.

  • First part. introduces the basic elements of machine learning and their applications in word-based text analysis;
  • Part two. includes structured representations of language (such as sequences, trees, and graphs);
  • Part three. explains different approaches to representing and analyzing linguistic meaning, from formal logic to neural word embedding;
  • Part four. describes three applications of NLP: information extraction, machine translation, and text generation.

This book is suitable for readers with more advanced computer programming and mathematics skills, including undergraduate and graduate students, academic researchers, NLP software engineers, and data scientists.

Authors:Steven Baird, Ewan Klein, Edward Loper

Natural Language Processing Book

This book is timeless, introducing readers to NLP with a focus on programming. Fans call it “The NLTK Book” because it’s very hands-on, somewhat ignoring theory, focusing on practical examples that explain how to use the library (while learning about key NLP concepts).

Given the approach, it is well suited for beginners. And the preface says it’s for anyone who wants to learn to write programs that parse written language (regardless of previous programming experience).

The book will teach you:

  • How to write simple programs to help you manage and analyze language data
  • Different algorithms and data structures used in NLP
  • Basic NLP and linguistic concepts used to describe and analyze language
  • How easy it is to work with common data formats used in NLP
  • How to evaluate NLP performance

Author: Yoav Goldberg

Natural Language Processing Book

Yoav Goldberg’s main focus is to detail neural networks and their applications in NLP. In doing so, he shares some additional resources to help readers further expand their knowledge once they finish his book.

The book is divided into four chapters, with the first half covering the basics of supervised machine learning, advanced neural networks, and working with natural language data. The second half then focuses on the architecture of more specialized neural networks.

Goldberg’s approach is to help you apply technology to your favorite language development problems, instead of going through each topic in detail. That’s why we recommend developers and industry professionals with neural network experience to read this one.

Authors:Nitin Indurkhya, Fred J. Damerau

Natural Language Processing Book

Handbook of Natural Language Processing presents tools and techniques for developing and implementing effective NLP in computer systems.

The book deals with the topic in three sections.

  • Part one. Traditional methods, including symbolic and empirical approaches;
  • Section two. Statistical approaches;
  • Section three. Many applications from information visualization to ontology building and biomedical text mining.

The second edition of the paper has a multilingual scope and focuses more on statistical approaches. It is a great starting point for beginners who want to learn how to apply NLP to computer systems.

Note: there is also an online version of the manual found here.

Authors:Hobson Lane, Cole Howard, Hannes Hapke

NLP books

This resource is more of a guide to building machines that read and interpret human language.

The book develops traditional NLP approaches that incorporate neural networks, modern deep learning algorithms, and generative methods as you tackle real-world challenges like extracting dates, composing text, and answering free-form questions.

The book consists of three parts.

  • Talking machinesTo give the reader an understanding of NLP, tokenization, TF-IDF vectors and semantic analysis;
  • Deep learning. focusing on DL and neural networks, including CNNs and RNNs;
  • Getting reality. to fight challenges such as information extraction, dialog engines and extension.

Authors:Daniel Jurafsky, James H. Martin

Natural Language Processing Book

We’ll finish with an older book that, despite its publication date, is still one of the most recommended NLP books.

Professors from Stanford University and the University of Colorado co-authored the text as a deep dive into language processing. And the book offers a unified vision of speech and language development while covering both statistical and symbolic approaches to language development.

It also introduces algorithms and methods for speech recognition, data mining, search engines, and the creation of spoken dialog agents, making it a must-read for both beginners and those interested in learning about the theory and applications of language processing.

Note: there is also an online version of the book found hereand you can find updated information on it Stanford University website.

Interested in learning more? Why not read the DLabs.AI article 7 Key Benefits of Using Natural Language Processing in Business — and if you know of a book we’ve missed from the list, hit us up on social media and we’ll consider adding it to the list.

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