As momentum for machine learning and artificial intelligence accelerates, natural language processing (NLP) plays a more prominent role in bridging computer and human communication. Increased attention with NLP means more online resources are available, but sometimes a good book is needed to get grounded in a subject this complex and multi-faceted. Books can increase your overall data literacy and contain fundamental background offering readers a great introduction to NLP or clarity on major theories and real-life examples.
Here are eight great books to broaden your knowledge and become familiar with the opportunities that NLP creates for individuals, business, and society. It satisfies all analytics skill levels.
One of the most widely referenced and recommended NLP books, written by Stanford University professor Dan Jurafsky and University of Colorado professor James Martin, provides a deep-dive guide on the subject of language processing. It’s intended to accompany undergraduate or advanced graduate courses in NLP or Computational Linguistics. However, it’s a must-read for anyone diving into the theory and application of language processing as they grow and strengthen their analytics capabilities.
This is the second edition and Jurafsky and Martin are working on the third with a targeted completion later this year. View a draft on Jurafsky’s Stanford web page.
This book is another introductory guide to NLP and considered a classic. While it was published in 1994, it’s highly relevant to today’s discussions and analytics activities and lauded by generations of NLP researchers and educators. It introduces major techniques and concepts required to build NLP systems, and goes into the background and theory of each without overwhelming readers in technical jargon.
Authors: Nitin Indurkhya and Fred J. Damerau
This comprehensive, modern “Handbook of Natural Language Processing” offers tools and techniques for developing and implementing practical NLP in computer systems. There are three sections to the book: classical techniques (including symbolic and empirical approaches), statistical approaches in NLP, and multiple applications—from information visualization to ontology construction and biomedical text mining.
The second edition has a multilingual scope, accommodating European and Asian languages besides English, plus there’s greater emphasis on statistical approaches. Furthermore, it features a new applications section discussing emerging areas such as sentiment analysis. It’s a great start to learn how to apply NLP to computer systems.
Authors: Alexander Clark, Chris Fox, and Shalom Lappin
Similar to the “Handbook of Natural Language Processing,” this book includes an overview of concepts, methodologies, and applications in NLP and Computational Linguistics, presented in an accessible, easy-to-understand way. It features an introduction to major theoretical issues and the central engineering applications that NLP work has produced to drive the discipline forward. Theories and applications work hand in hand to show the relationship in language research as noted by top NLP researchers. It’s a great resource for NLP students and engineers developing NLP applications in labs at software companies.
Author: Ruslan Mitkov
This handbook describes major concepts, methods, and applications in computational linguistics in a way that undergraduates and non-specialists can comprehend. As described on Amazon, it’s a state-of-the-art reference to one of the most active and productive fields in linguistics. A wide range of linguists and researchers in fields such as informatics, artificial intelligence, language engineering, and cognitive science will find it interesting and practical. It begins with linguistic fundamentals, followed by an overview of current tasks, techniques, and tools in Natural Language Processing that target more experienced computational language researchers. Whether you’re a non-specialist or post-doctoral worker, this book will be useful.
Another book that hails from Stanford educators, this one is written by Jurafsky’s colleague, Christopher Manning. They’ve taught the popular NLP introductory course at Stanford. Manning’s co-author is a professor of Computational Linguistics at the German Ludwig-Maximilians-Universität.
The book provides an introduction to statistical methods for NLP and a decent foundation to comprehend new NLP methods and support the creation of NLP tools. Mathematical and linguistic foundations, plus statistical methods, are equally represented in a way that supports readers in creating language processing applications.
This book is a helpful introduction to the NLP field with a focus on programming. If you want have a practical source on your shelf or desk, whether you’re a NLP beginner, computational linguist or AI developer, it contains hundreds of fully-worked examples and graded exercises that bring NLP to life. It can be used for individual study, as a course textbook when studying NLP or computational linguistics, or in complement with artificial intelligence, text mining, or corpus linguistics courses.
Curious about Python programming language? It will walk you through creating Python programs that parse unstructured data like language and recommends downloading Python and the Natural Language Toolkit. On a companion site, the authors have actually updated the book to work with Python 3 and NLTK 3.
8. “Big Data Analytics Methods: Modern Analytics Techniques for the 21st Century: The Data Scientist’s Manual to Data Mining, Deep Learning & Natural Language Processing”
Author: Peter Ghavami
Peter’s book might seem daunting to a NLP newcomer, but it’s useful as a comprehensive manual for those familiar with NLP and how big data relates in today’s world. It also works as a helpful reference for data scientists, analysts, business managers, and Business Intelligence practitioners. With more than a hundred analytics techniques and methods included, we think this will be a favorite for seasoned analytics practitioners.
Chapters cover everything from machine learning to predictive modeling and cluster analysis. Data science topics including data visualization, prediction, and regression analysis, plus NLP-related fields such as neural networks, deep learning, and artificial intelligence are also discussed. These come with a broad explanation, but Peter goes into more detail about terminology and mathematical foundations, too.
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