Top 10 NLP Books

Top 10 NLP Books

Posted on 02/03/2020

Top 10 NLP Books

Intro

Natural Language Processing (NLP), is a subset of artificial intelligence, linguistics, computer science, and data engineering. NLP experts are required to study and apply computational methods of bringing together computer and human communication by processing speech and text data. As investments into machine learning (ML) and artificial intelligence (AI) increase every year, natural language processing (NLP) plays a significant role in creating predictive models that can be applied to day to day business problems. Increased focus on NLP has led to an increase in online resources about the subject. However, to get a genuine grasp of a subject so complicated, students must focus on books rather than online sources. NLP Books will increase your overall knowledge about the subject, providing readers with a strong understanding of key theories and how they can be applied to solve real-life problems.

Discussed below are the top 10 books on natural language processing that will provide NLP enthusiasts with a clear theoretical understanding of the subject.

1. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit- by Steven Bird, Ewan Klein, and Edward Loper

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This book serves as the ideal introductory book to the field of NLP. Focused primarily on Python programming, what makes the book stand-out are the brilliant examples scattered across each chapter that make understanding basic Python programs on how to analyze unstructured data, very easy.  There are also graded exercises that focus on improving the reader’s computational linguistics, text mining, and Python programming language. This is a must-read for NLP beginners, aspiring computational linguists and AI/ML program developers.

2. Text Mining with R - by Julia Silge and David Robinson

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Text and data mining is a method of deriving information from machine-read raw data. The process involves copying large amounts of raw machine-read material, obtaining the data, and re-fusing the data to recognize patterns. TDM is an important aspect of NLP and Text Mining with R is a great book to dive into the subject.
This book explains statistical NLP methods using examples of how it can be used for modern applications. The code used in the book focuses on ‘The Tidy Text Format’, a data handling technique developed by Hadley Wickham. This is an ideal book for NLP beginners who want to gain a clearer understanding of important NLP subjects such as Sentiment Analysis, Topic Modeling and how to convert data to and from non-tidy formats.

3. Foundations of Statistical Natural Language Processing - by Christopher Manning and Hinrich Schütze

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Published in 1999, this book is one of the oldest and the most important works on how to implement statistical structures for NLP. Covering important topics on linguistics and statistical methods, for NLP beginners with no prior knowledge of linguistics/statistics, this is the best book. The book will give you a strong theoretical understanding of linguistics, statistical methods and encoding the two.

4. Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

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A helpful read for software developers with little experience in ML, this test and practice-based book is not exclusively focused on NLP, but it gives readers a complete outline of some of the most important developments in ML. With chapters dedicated to neural networks, simple linear regression, etc. any AI or ML enthusiast will be able to enjoy this book.

5. Deep Learning in Natural Language Processing- by Li Deng and Yang Liu

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The book offers an interesting look into the world of advanced deep learning methods and their effective applications to key NLP tasks, for example, lexical analysis, speech recognition and understanding machine translation, sentiment analysis, and a lot more. The technical language used in the book maybe difficult to understand for beginners. The book is targeted more towards advanced computational linguistic graduates, in addition to academic researchers. For NLP enthusiasts wanting to gain a basic understanding of deep learning and its practical applications, this book is a good read.

6. The Handbook of Computational Linguistics and Natural Language Processing by Alexander Clark, Chris Fox, and Shalom Lappin

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As the title suggests, this book gives readers an easy to understand summary of ideas, practices, and applications in Computational Linguistics and NLP. Introducing readers to some of the most significant theoretical topics in NLP, the book is focused on highlighting the central engineering applications that NLP experts from around the world have worked on in order to advance the discipline. The theories and applications presented in the book help readers gain a better understanding of the core concepts of important language research work done by top NLP researchers. For NLP students and software engineering graduates, this is a helpful handbook. 

7. Taming Text - by Grant Ingersoll, Thomas Morton and Drew Farris

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This is another must-read introductory book to numerous NLP tools and applications. Readers will get to know about important tools like Apache Mahout, Apache Solr, and Apache Open NLP. The code samples are in Java (yes, finally a book using Java) so any amateur software student will be able to understand the book. Offering a first-hand look into the famous Apache Software Foundation, for any aspiring NLP amateur wanting to familiarize him/herself with company-level NLP tools, this is the best book to start your journey.

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 by Peter Ghavami

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This book serves as a detailed manual for readers with advanced knowledge in NLP, focusing on big data analytics with real-world examples. It is a valuable source for data analysts and Business Intelligence experts. The book has detailed chapters on data science, ML, data visualization, predictive modeling, regression analysis and cluster analysis. The book also dives deep into broader topics like deep learning, neural networks, and AI. Readers will also get to understand the core mathematical foundations of NLP as well.
This book is not meant for NLP beginners as only experienced analytics practitioners will be able to appreciate and understand the hundreds of analytics techniques and methods demonstrated in the book. A must-read for advanced students of NLP, this book also offers a huge list of important NLP terminology that is bound to come in handy.

9. Speech and Language Processing by Daniel Jurafsky and James H. Martin

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One of the most extensively referenced and endorsed books on NLP, ‘Speech and Language Processing’ is a comprehensive guide on the discipline of language processing.
A commonly used textbook for Computational Linguistics graduates; this is a must-read for any NLP enthusiast wanting to explore the theories of language processing. Readers will be able to cultivate their analytics skills and gain a strong foundational understanding of linguistics and its evolving relationship with computer science. Written by two leading professors of NLP, Dan Jurafsky (Stanford University) and James Martin (University of Colorado), the third updated edition of this book is expected to be released very soon.

10. The Oxford Handbook of Computational Linguistics - by Ruslan Mitkov

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This handbook explains some of the most important concepts of computational linguistics. The author takes a purely academic approach offering readers multiple important linguistic theories and fundamentals. This book is a must-read for researchers in the field of informatics, AI, language engineering, and Linguistics. By the end of the book, readers will be educated on some of the most important tools in Natural Language Processing.

Conclusion

Although NLP is an extremely exciting field, a lot of newcomers are discouraged from diving deep into this discipline because of the countless sub-problems and complex terminology used in these books. This is why having a mixture of reference texts and introductory books on your shelf, is very important. By reading these 10 books, you will be able to gain a fundamental understanding of some of the core concepts on NLP. You’ll also get to explore hundreds of theories backed with real-life examples.  

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