Best NLP Books- What Data Scientists Must Read in 2024?

Going to drill into NLP? Here we have got a stack of the best NLP books for you to read and test the waters before jumping in.

Best NLP Books- What Data Scientists Must Read in 2024?
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So many NLP books, so little time - the problem of choice arises when you want to become a better data scientist, NLP engineer, or machine learning engineer by drenching in some top NLP books. You might have come across several Blurbs written to make you buy every NLP book but not to help you choose the best books on NLP that can help you learn NLP from scratch.


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Our Top 15 NLP Books To Read Right Now

Reading books on various NLP techniques from different authors is essential to build a rounded and exhaustive knowledge base. Books on NLP are abundant on the internet, making it harder for beginners to pick a book. We have a great list of the best books on NLP, in no way ultimate, but definitely worth your attention if you want to learn NLP. You can also check which ones you have already completed among these top NLP books.

And here we go.

 

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3 Best NLP Books for Beginners

Find below the best books on NLP for beginners: 

Natural Language Processing Crash Course for Beginners: Theory and Applications of NLP using TensorFlow 2.0 and Keras

 

This NLP book is a good option for beginners to start their learning journey as it covers the theoretical and practical aspects of natural language processing. It gives you a step-by-step breakdown of installing the software required to put the numerous NLP techniques into practice. The extremely brief introduction to the Python programming language in the second chapter will be quite beneficial, even if you are a complete beginner. 

Exclusive Topics Covered

  • Introduction to Natural Language Processing 

  • Environment setup 

  • NLP Tasks

  • Text cleaning and manipulation 

  • Word Embeddings: Converting Words to Numbers

  • Text summarization 

  • Text classification with deep learning 

  • Hands-on Projects  

Why Read Natural Language Processing Crash Course for Beginners? 

This book covers a list of hands-on NLP projects that can help you apply NLP methods for practical applications. Also, the book provides access to all the additional learning resources—Python programs, exercises, and PDFs. 

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Most Popular Review of Natural Language Processing Crash Course for Beginners

The book explains the theory with practical examples and exercises. Author Usman understands the critical issues to be learned at the beginning. I think his expertise in the field is convincing. I consider this book as a day-to-day cookbook giving you a pragmatic view while building any system and a stepping stone to broaden the application.” - Experienced Professional. 

Getting Started with Natural Language Processing: A friendly introduction using Python - Ekaterina Kochmar 

 

This book is for beginners who are new to NLP and want to improve their applications with features and functions like information extraction, user profiling, and automatic subject labeling. Each chapter of this book includes a specific example with useful approaches that you can use right away, along with Python code and hands-on projects. 

Exclusive Topics Covered

  • Basic concepts and NLP algorithms 

  • Information search 

  • Information extraction 

  • Sentimental analysis

  • Topic analysis

  • Topic modeling

Why Read Getting Started with Natural Language Processing?

This NLP Book covers clear explanations with practical examples. As you progress, each new project adds to your prior knowledge while providing fresh ideas and skills. Even if you have no prior experience with machine learning, getting started is simple because of the helpful graphics and clear Python code samples!

Most Popular Review of Getting Started with Natural Language Processing

“The content looks good, and the writing seems clear and well constructed.” - Experienced Professional. 

Handbook of Natural Language Processing -Nitin Indurkhya, Fred J Damerau  

 

The Handbook of Natural Language Processing discusses many approaches and techniques, divided mainly into the Classical, Empirical & Statistical Approaches, and Applications. The Classical Approach covers Lexical analysis, Syntactic Parsing, semantic analysis, and Natural Language Generation. While under Empirical and Statistical Approaches, the book lists corpus creation, treebank annotation, statistical machine translation, etc. 

Exclusive Topics Covered 

  • Classical approaches 

  • Empirical and Statistical Approaches

  • NLP Applications   

Why Read Handbook of Natural Language Processing

The book provides many practical applications that use the concepts discussed previously. These NLP applications include report generation, BioNLP, Ontology construction, sentiment analysis, etc. 

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3 Best NLP Books on Python  

Find below the best natural language processing books on Python: 

Natural Language Processing with Python -Steven Bird, Ewan Klein, and Edward Loper

 

The book takes a practical step-by-step approach to introduce NLP concepts to the user through many chapters. Natural Language ToolKit or NLTK is used in the book extensively to explain theories and show essential techniques. The flow in the book starts from the basics. It builds onto more complex NLP techniques, including tagging words, processing raw text, building feature-based grammar, analyzing sentence structure and semantics, etc. One also learns in-depth conversion between tidy and non-tidy data formats, as provided by R. 

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Exclusive Topics Covered

  • Language Processing and Python

  • Processing raw text

  • Writing structured programs

  • Analyzing Sentence Structure 

  • Learn to Classify Text 

  • Analyze Sentence Structure 

  • Manage Linguistic Data 

Why Read Natural Language Processing with Python?

Along with providing NLP knowledge, the book also teaches the basics of python that one needs to implement NLP solutions. The documentation available in the book complements the concepts remarkably and should be noticed. It is rated a whopping 4.3 on Goodreads. 

Most Popular Review of Natural Language Processing with Python

“I have been using this book to help me with my final year project on text mining in a Computer Science course, and I love it! It was overwhelming at first because I was brand new to Python and natural language processing, but after I learned a bit more about the topics, the book became very helpful for me, and I use it almost every day at the moment.” - A graduate student

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Natural Language Processing with PyTorch - by Delip Rao, Brian McMahan

 

This book provides a solid understanding of NLP and deep learning techniques. Also, it shows you how to use NLP and deep learning techniques using PyTorch, a Python-based deep learning framework, for developers and data scientists who are new to these techniques.

Exclusive Topics Covered 

  • Foundational components of neural networks 

  • Feed-Forward Networks for NLP 

  • Embedding Words and Types 

  • Sequence Modeling for NLP 

  • Intermediate Sequence Modeling for NLP

  • Advanced Sequence Modeling for NLP

  • Classics and Frontiers 

Why Read Natural Language Processing with PyTorch? 

Each chapter of this NLP book provides several codes and examples. If you're a developer or data scientist new to NLP and deep learning, this practical book will teach you how to use PyTorch, a Python-based deep learning framework, to implement these approaches.

Most Popular Review of Natural Language Processing with PyTorch

“This book teaches NLP basics from the ground up, along with a strong design pattern coded in python/PyTorch. It teaches it seamlessly by starting from a simple example and continuing with other more advanced examples that keep using the same design pattern over and over again. For me, this is the best way to learn and remember. It has given me a foundation on how to sit down and code my own solution in an organized fashion using proper python object-oriented practices.” - Experienced Professional

Hands-on Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications 

 

This book is for you if you are a developer, NLP engineer, or a machine learning engineer who wants to build a deep learning application using NLP techniques. This book is also ideal for deep learning users who wish to apply their deep learning skills to develop NLP applications. A basic understanding of python and machine learning is useful for reading this book.  

Exclusive Topics Covered

  • Semantic embedding of words to classify and find entities

  • Convert words to vectors using training to perform arithmetic operations.

  • Train a deep learning network to recognize tweet and news categorization.

  • Create a question-answer model using search and RNN models.

  • Convert voice-to-text and text-to-voice 

Why Read Hands-on Natural Language Processing with Python?

This book provides practical skills to integrate deep learning in your language applications using TensorFlow, Python's famous deep learning library.

Most Popular Review of Hands-on Natural Language Processing with Python 

“Comprehensive coverage of NLP capability - This book’s coverage of things you can do to text data using natural language processing is excellent! It is quite a menu to choose from. It assumes you know Python but says so at the beginning. Hence, the negative reviews aren’t valid.” - an Experienced Professional. 

3 Best NLP Books on Goodreads 

Find below the best-selling NLP books on Goodreads: 

Natural Language Processing in Action - Hobson Lane and Hannes Hapke

 

As the name suggests, the book explores NLP with the help of adequate examples and tasks demonstrating the concepts in action. It builds from simpler concepts like word vector, tokenization, dimension reduction, and launches into more meaty techniques such as Neural Networks, Deep Learning, and Encoders-Decoders. 

Exclusive Topics Covered 

  • Wordy Machines 

  • Deeper Learning - Neural Networks 

  • Real-World NLP Challenges 

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Why Read Natural Language Processing in Action?

You will get to learn how to build a search engine that uses the semantics of the keyword part from the syntax. Additionally, one will also build a deep-learning chatbot that can make conversations and reply to queries asked. One reviewer on Goodreads likened the book to a recipe book given to the multitude of examples in it. 

Most Popular Review of Natural Language Processing in Action 

“I read this textbook almost cover to cover (which is very rare with technical textbooks) as it was very well written and enjoyable to read. I feel like I have gotten a very good understanding of the challenges and subtleties of natural language processing (and I know where to look if I need more details about a specific thing). The tutorials/code examples are also very clear and helpful - it helps a lot to code along when you read and experiment!” - an Experienced Professional. 

Foundation of Statistical Natural Language Processing - Christopher D Manning and Hinrich Schütze  

 

The book explores the statistical approach to Natural Language Processing and helps users master the mathematics and linguistics needed. It focuses on statistical methods and techniques that have recently become popular. The book goes exhaustive in mathematical models, creates a solid foundation for the reader to understand new NLP methods, and supports the development of NLP tools. 

Exclusive Topics Covered

  • Mathematical Foundations 

  • Linguistic Essentials 

  • Corpus-Based Work

  • Collocations 

  • Statistical Inference: n-gram Models over Sparse Data

  • Word Sense Disambiguation

  • Lexical Acquisition 

  • Markov Models 

  • Probabilistic Parsing

  • Statistical Alignment and Machine Translation 

  • Clustering

  • Text Categorization

Why Read the Foundation of Statistical Natural Language Processing?

The author, Christopher D Manning, is an eminent professor of Linguistics and Computer Science at Stanford University. It contains topics like probabilistic parsing, Information Retrieval, Machine Translation, and word sense disambiguation.

Most Popular Review of Foundation of Statistical Natural Language Processing

"Statistical natural-language processing is, in my estimation, one of the most fast-moving and exciting areas of computer science these days. Anyone who wants to learn this field would be well-advised to get this book. For that matter, the same goes for anyone who is already in the field. I know that it will be one of the most well-thumbed books on my bookshelf." - Eugene Charniak, Department of Computer Science, Brown University

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Neural Network Methods for Natural Language Processing

 

This book mainly focuses on how neural network methods are applied to natural language data. Based on its structure, it is for practitioners from both deep learning and natural language processing to have a shared knowledge of what has been achieved in these two fields. This book also helps NLP practitioners to become well-equipped with neural network tools to work on their natural language data. 

Exclusive Topics Covered 

  • Supervised Classification and Feed-Forward Neural Networks 

  • Working with Natural Language Data 

  • Specialized Architectures

  • Modeling Trees with Recursive Neural Networks

  • Structured Output Prediction

  • Cascaded, Multi-task, and Semi-supervised Learning 

Why Read Neural Network Methods for Natural Language Processing? 

From a neural network entry, the structure of the book seems smoother with this guideline: It addresses the characteristics of natural language data, including problems to solve and sources of information that we might use so that specialized neural network models introduced later are built in ways that accommodate natural language data.

Most Popular Review of Neural Network Methods for Natural Language Processing

“Provides a meaningful overview of NLP techniques in the field of ML and specifically Neural Network solutions and approaches. This provided me with a good survey of various approaches, was thorough enough to allow me to make assessments about where to look next, and was also broad enough to put this book down, feeling that I had learned a lot. Highly recommend this book for folks without formal training but interested in NLP and Neural Networks.” - an Experienced Professional.

3 Best NLP Free Books 

Find below the best free NLP books: 

Text Mining in R - Julia Silge and David Robinson

‘Text Mining in R’ is a holy grail for using R in NLP and text mining. The book comes abundant with code examples, data exploration, and analyses that extensively use tide tools and data frames present in R. You will learn to use the tidytext package developed by the authors of the book - Julia Silge and David Robinson. 

Exclusive Topics Covered

  • The tidy text format 

  • Sentimental Analysis with Tidy Data 

  • Analyzing Word and Document Frequency: tf-idf 

  • Relationships between words: N-grams and correlations 

  • Converting to and from Nontidy Formats 

  • Topic Modeling 

Why Read Text Mining in R?

The online version of the book is completely free. One can audit it before investing in a paperback. Additionally, the book features three case studies with Twitter and NASA data that help demonstrate the techniques discussed in use. The case studies perform mining on Twitter, Usenet, and NASA data. 

Most Popular Review of Text Mining in R

“This is an excellent book about text mining. I would highlight the clear and concise explanation given by the authors, the relevance of the examples, and the book's structure. Furthermore, the authors must base their analyses on the tidy approach to data analysis (a framework of concepts that is rapidly becoming the standard approach in R). The best chapters are the three fleshed-out examples in the last chapters.” - an Experienced Professional  

Taming Text: How to Find, Organize and Manipulate It

 

Taming Text is a hands-on, example-driven guide on dealing with unstructured text in the context of real-world applications. This book explores how to arrange text automatically using methods such as full-text search, proper name recognition, information extraction, clustering, tagging, and summarization. The book also walks you through examples that illustrate each of these subjects and the foundations that support them. 

Exclusive Topics Covered

  • Foundations of Taming Text

  • Fuzzy String Matching 

  • Identifying People, Places, and Things 

  • Clustering Text

  • Classification, Categorization, and Tagging 

  • Building an example question-answering system

  • Untamed text 

Why Read Taming Text: How to Find, Organize and Manipulate It?

It is an excellent book written by three expert professionals: Grant Ingersoll (engineer, speaker, and trainer, a Lucenecommitter, and a co-founder of the Mahout machine-learning project). Thomas Morton, the primary developer of OpenNLP and Maximum Entropy. Drew Farris, technology consultant, software developer, and contributor to Mahout, Lucene, and Solr.

Most Popular Review of Taming Text: How to Find, Organize and Manipulate It

“This book is down-to-earth with a perfect balance of depth of the subject and practical applications. I have intermediate level NLP experience, but this book still delivers at this level with easy to read style despite the very technical basis of the topics.” - Experienced Professional. 

Speech and Language Processing-Dan Jurafsky and James H Martin

 

A massive book of 940 pages and almost 1.5 kgs in mass, Speech and Language Processing is a masterpiece in Language Processing in its own right. An ideal for NLP researchers, it also stands popular among beginners in the field. It presents state-of-the-art techniques and algorithms in both speech as well as text processing. It delves into key concepts like Neural Networks, text-to-speech, WordNet, chatbots, hidden Markov models, Machine Translation, etc.

Exclusive Topics Covered

  • Regular Expressions and Automata

  • Morphology and Finite-State Transducers

  • Computational Phonology and Text-to-Speech

  • Probabilistic Models of Pronunciation and Spelling

  •  HMMs and Speech Recognition

  • Word Classes and Part-of-Speech Tagging

  • Context-Free Grammars for English

  • Parsing with Context-Free Grammar

  • Lexicalized and Probabilistic Parsing

  • Language and Complexity

  • Semantic Analysis 

  • Lexical Semantics

  • Machine Translation 

Why Read Speech and Language Processing

The author, Dan Jurafsky, is an esteemed professor at Stanford of Linguistics. He is also the recipient of the MacArthur Genius grant. The book is rated 4.2 on Goodreads. 

Most Popular Review of Speech and Language Processing

“This is an excellent introduction to the really complex subject; It definitely isn't an easy reading, but much easier for understanding than most of the text on NLP subject I ever read.” - an Experienced Professional.  

3 Best NLP Books on Amazon 

Find below the best NLP Books on Amazon: 

Sentiment Analysis and Opinion Mining - Bing Liu

This book expands on Opinion mining or Sentiment analysis extracted from written language by citing more than 600 papers on the topic. It presents state-of-the-art algorithms and techniques used by the big corps, like new broadcasting media and advertising media such as Conditional Random Fields, Support Vector Machines, etc. A pdf-version floats freely on the internet, varying slightly from the printed copy. 

Exclusive Topics Covered

  • Sentiment Analysis Research

  • Document Sentiment Classification

  • Sentence Subjectivity and Sentiment Classification

  • Aspect-based Sentiment Analysis

  • Sentiment Lexicon Generation

  • Opinion Summarization

  • Analysis of Comparative Opinions

  • Opinion Search and Retrieval

  • Opinion Spam Detection 

Why Read Sentiment Analysis and Opinion Mining?

Sentiment Analysis and Opinion mining is a subliminal text on a subject that influences our public behaviors and idea of reality. We, as humans, are shaped by the opinions of our fellow mates, whom we influence. It's a complex problem to nail down, and the book tries to do the same, acting as an introductory text and research survey. This book on NLP is equally propounded by researchers, teachers, students, and industry experts alike. 

Most Popular Review of Sentiment Analysis and Opinion Mining

“I like this book. It gave me a good view of sentiment analysis. It contains almost all papers of Bing Liu and approaches to different aspects of sentiment analysis. I suggest others read it if they want to be experts in opinion mining. Also, it can motivate the reader if he wants to write a paper or thesis in this area.” - an Experienced Professional.  

Natural Language Processing with Java - Richard M Resse and Ashish Singh Bhatia

 

The book uses JAVA to teach NLP with the help of JAVA libraries like CoreNLP, OpenNLP, Neuroph, and Mallet. The text is a boon for anyone already well-versed with programming in JAVA, as most of the older coders would be. Concepts like text summarization, word tagging, dialogue systems, language parsing, and machine translation are explained in detail. 

Exclusive Topics Covered

  • Introduction to NLP

  • Finding Parts of Text

  • Finding Sentences

  • Finding People and Things

  • Detecting Parts of Speech

  • Representing text with features

  • Information retrieval

  • Classifying Texts and Documents

  • Topic Modeling

  • Using Parser to Extract Relationships

  • Combined Pipeline

  • Creating chat Bot

Why Read Natural Language Processing with Java?

Most legacy systems use C++ and Java in their programs. Software developers who are proficient in Java can learn Language Processing without needing to learn a new language. This is quite efficient as most older generation coders know JAVA already.

Introduction to Information Retrieval- Christopher D Manning

Introduction to Information Retrieval specifies the nooks and crannies of Information Retrieval in the age of Web searching and the Internet. It provides a practical way into the course with examples and relevant figures interspersed throughout the text. It covers concepts like web crawling, link analysis, hierarchical clustering, and latent semantic indexing. 

Exclusive Topics Covered

  • Boolean retrieval 

  • Index construction 

  • Index compression

  • Computing scores in a complete search system

  • XML retrieval

  • Probabilistic information retrieval 

  • Vector space classification

  • Support vector machines and machine learning on documents

  • Flat and Hierarchical clustering

  • Matrix decompositions and latent semantic indexing

Why Read Introduction to Information Retrieval?  

It is the second book on this list by the authors Christopher D Manning, Hinrich Schutze, and Prabhakar Raghvan. It focuses on information gathering, indexing, and searching over the internet. The book has been molded through many classroom experiences, making it more interactive and problem-oriented.  

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Most Popular Review of Introduction to Information Retrieval

This is the first book that gives you a complete picture of the complications that arise in building a modern web-scale search engine. You'll learn about ranking SVMs, XML, DNS, and LSI. You'll discover the seedy underworld of spam, cloaking, and doorway pages. You'll see how MapReduce and other approaches to parallelism allow us to go beyond megabytes and to efficiently manage petabytes.' - Peter Norvig, Director of Research, Google Inc.

Key Takeaways 

It is tempting to keep your nose buried in books to learn the theoretical concepts of NLP. But it is also important to realize that books are time-consuming affairs and do not give you the hands-on knowledge to implement various NLP strategies, tools, techniques, and algorithms in a real-world business problem. A quick and effective way to assimilate NLP knowledge is to attempt NLP projects hands-on. Getting one’s hands dirty will increase the understanding of NLP concepts manifold. Projects add more credibility to your data science resume and help you get hired with highly rewarding perks.

FAQs on NLP Books 

The best-selling NLP books are 

  1. Foundation of Statistical Natural Language Processing - Christopher D Manning and Hinrich Schütze

  2. Speech and Language Processing-Dan Jurafsky and James H Martin 

  3. Introduction to Information Retrieval- Christopher D Manning 

The best NLP books for beginners are 

  1. Natural Language Processing Crash Course for Beginners: Theory and Applications of NLP using TensorFlow 2.0 and Keras

  2. Getting Started with Natural Language Processing: A friendly introduction using Python - Ekaterina Kochmar 

  3. Handbook of Natural Language Processing -Nitin Indurkhya, Fred J Damerau.

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