10 Best Books for Natural Language Processing You Should Read in 2024

Are you looking for Best Books for Natural Language Processing?. If yes, then your search will end after reading this article. In this article, I will discuss the 10 Best Books for Natural Language Processing. So, give your few minutes to this article and find out the Best Books for Natural Language Processing.

So without any further ado, let’s get started-

Best Books for Natural Language Processing

The aim of Natural Language Processing is that computers and machines can also communicate like humans. In short, Natural Language Processing is a part of Artificial Intelligence that deals with speech and text data.

NLP is important because of mainly two reasons-

• NLP can handle a large amount of text data.
• Another one is NLP can structure highly unstructured data.

There are various books available in the market. I have chosen some best books for natural language processing that cover the theoretical as well as the practical parts of Natural Language Processing. These books are ranging from beginners to experts level.

So, let’s get started-

1. Natural Language Processing with Python

Author- Steven Bird

This is one of the Best books for Natural Language Processing. This book will give you an introduction to Natural Language Processing using Python and Python NLTK Library.

With the help of this book, you will learn how to write Python programs that work with large collections of unstructured text.

Content of the Book-

1. Language Processing and Python
2. Accessing Text Corpora and Lexical Resources
3. Processing Raw Text
4. Writing Structured Programs
5. Categorizing and Tagging Words
6. Learning to Classify Text
7. Extracting Information from Text
8. Analyzing Sentence Structure
9. Building Feature-Based GRammars
10. Analyzing the Meaning of Sentences
11. Managing Linguistic Data

Why this book is good?

This book uses a practical approach instead of a theoretical approach. In that book, you will find enough code to start your journey in NLP.

Who Should Buy this Book?

• Those who are a beginner in Natural Language Processing.
• If someone wants to use NLTK Library.

Where to find this Book?

• You can buy this book on Amazon- Natural Language Processing with Python
• Or get Natural Language Processing with Python (Free Version).

2. Speech and Language Processing

Author- James H. Martin | Dan Jurafsky 

This is one of the most widely referenced and recommended books for Natural Language Processing. This book is written by Stanford University professor Dan Jurafsky and University of Colorado professor James Martin.

This is a must-read book for anyone who wants to dive into NLP. I personally love this book. This book covers a huge number of topics in NLP and elaborates on each topic.

Content of the Book-

1. Introduction
2. Regular Expressions and Automata
3. Words and Transducers
4. N-grams
5. Part-of-Speech Tagging
6. Hidden Markov and Maximum Entropy Models
7. Phonetics
8. Speech Synthesis
9. Automatic Speech Recognition
10. Speech Recognition: Advanced Topics
11. Computational Phonology
12. Formal Grammars of English
13. Syntactic Parsing
14. Statistical Parsing
15. Features and Unification
16. Language and Complexity
17. The Representation of Meaning
18. Computational Semantics
19. Lexical Semantics
20. Computational Lexical Semantics
21. Computational Discourse
22. Information Extraction
23. Question Answering and Summarization
24. Dialog and Conversational Agents
25. Machine Translation

Why this Book is Good?

This book is very clear, easy to read, and informative. It will provide in-depth knowledge of linguistics, computer science, and statistics.

Who Should Read this book?

• Who is complete beginner in Natural Language Processing.

Where to find this Book?

• You can Buy this book on Amazon- Speech and Language Processing

3. Foundations of Statistical Natural Language Processing

Author- Christopher Manning and Hinrich Schütze.

This book will give you an in-depth introduction to statistical methods for NLP. The book contains all the theories and algorithms needed for building NLP tools.

In that book, you will learn collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Why this Book is Good?

This book will give a complete information to statistical methods and linguistics for NLP.

Who should Read this Book?

• A complete beginner in NLP who don’t have any knowledge of linguistics or statistics.

Where to find this Book?

• You can buy this book on Amazon- Foundations of Statistical Natural Language Processing

4. Text Mining with R

Author- Julia Silge and David Robinson.

This book gives you an introduction to Text Mining with tidytext package and other tidy tools in R. This is one of the newest books and gives you a more practical and modern feel. In this book, you will learn how to use sentiment analysis to mine the emotional content of the text.

This book also covers case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages.

Why this Book is Good?

This book is new and focus on more practical approach.

Who should Read this Book?

• Who has basic understanding of R language.

Where to find this Book?

• You can buy this book on Amazon- Text Mining with R
• Book GitHub Repository (code and data)

5. Taming Text

Author- Grant Ingersoll, Thomas Morton and Drew Farris.

This book will give you an introduction to various NLP tools and problems, such as Apache Solr, Apache OpenNLP, and Apache Mahout. The programing language used in this book is Java.

This book also covers techniques like full-text search, proper name recognition, clustering, tagging, information extraction, and summarization.

Content of the Book-

• Getting started taming text
• Foundations of taming text
• Searching
• Fuzzy string matching
• Identifying people, places, and things
• Clustering text
• Classification, categorization, and tagging
• Building an example question answering system
• Untamed text: exploring the next frontier

Why this Book is Good?

This book provides first-hand insights into Apache-based NLP a cofounder of the Apache Mahout project. Along with that, this book uses Java code, which is very rare.

Who should read this book?

• Who want to get understanding of enterprise-grade NLP tools for work projects.

Where to find this Book?

• You can find this Book on Amazon- Taming Text.
• Book GitHub Repository (code and data).

6. Statistical Machine Translation

Author- Philipp Koehn.

This book will give you an introduction to statistical machine translation that is a sub-field of Natural Language Processing.

Content of the Book-

• Introduction
• Words, Sentences, Corpa
• Probability Theory
• Word-Based Models
• Phrase-Based Models
• Decoding
• Language Models
• Evaluation
• Discriminative Training
• Integrating Linguistic Information
• Tree-Based Methods

Why this Book is Good?

This book will provide all the theories and methods required to build a statistical machine translator, such as Google Language Tools.

Who Should Read this Book?

• Who want to learn about statistical machine translation and who are working on statistical machine translation (SMT).

Where to find this Book?

• You can buy this book on Amazon- Statistical Machine Translation

7. Statistical Methods for Speech Recognition

Author- Frederick Jelinek.

This book will give you an introduction to the topic of statistical speech recognition, another most popular application of Natural Language Processing. In that book, you will find a thorough introduction to speech recognition.

Content of the Book-

• The Speech Recognition Problem
• Hidden Markov Models
• The Acoustic Model
• Basic Language Modeling
• The Viterbi Search
• Hypothesis Search on a Tree and the Fast Match
• Elements of Information Theory
• The Complexity of Tasks – The Quality of Language Models
• The Expectation-Maximization Algorithm and Its Consequences
• Decision Trees and Tree Language Models
• Phonetics from Orthography: Spelling-to-Base Form Mappings
• Triphones and Allophones
• Maximum Entropy Probability Estimation and Language Models
• Tree Applications of Maximum Entropy Estimation to Language Modeling
• Estimation of Probabilities from Counts and the Back-Off Method

Why this Book is Good?

The author of this book is one of the ‘fathers’ of the field of NLP and Speech recognition. This book will teach you a lot about Hidden Markov Models and their use in Speech Recognition.

Who Should Read this Book?

• Who want to dive into statistical speech recognition.

Where to find this Book?

• You can buy this book on Amazon- Statistical Methods for Speech Recognition

8. Neural Network Methods in Natural Language Processing

Author- Yoav Goldberg , Graeme Hirst

This book covers the application of neural network models to natural language processing tasks. In that book, you will learn about the basics of supervised machine learning and feed-forward neural networks.

Along with that, you will also learn neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models.

Why this Book is Good?

This book will give you a thorough overview of state-of-the-art neural network models that is beneficial for NLP.

Who should Read this Book?

• This book is suitable for Software developers and industry practitioners who have basic knowledge of Neural Network.

Where to find this Book?

• You can buy this book on Amazon-Neural Network Methods in Natural Language Processing
• Free Sample of the Book- Neural Network Methods in Natural Language Processing

9. Applied Text Analysis with Python

Author- Benjamin Bengfort , Rebecca Bilbro , Tony Ojeda

In that book, you will learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering.

Along with that, you will also learn how to perform document classification and topic modeling. This book will teach you how to build a dialog framework to enable chatbots and language-driven interaction.

Why this Book is Good?

This book will give you a data scientist’s perspective on building language-aware products with applied machine learning techniques.

Who Should read this Book?

• This book is suitable for Software Developers in Python, who want to apply natural language processing and machine learning to their software development toolkit.

Where to find this Book?

• You can buy this book on Amazon- Applied Text Analysis with Python

10. Natural Language Processing in Action

Author- Hobson Lane, Hannes Hapke, Cole Howard

As its name suggests “Natural Language Processing in Action”, it’s another best practical approach book for NLP. In that book, you will learn how to build machines that can read and interpret human language.

The authors of this book are experienced NLP engineers. In that book, you will learn how to use readily available Python packages to capture the meaning in the text and react accordingly.

Content of the Book-

• PART 1 – WORDY MACHINES
  • Packets of thought (NLP overview)
  • Build your vocabulary (word tokenization)
  • Math with words (TF-IDF vectors)
  • Finding meaning in word counts (semantic analysis)
• PART 2 – DEEPER LEARNING (NEURAL NETWORKS)
  • Baby steps with neural networks (perceptrons and backpropagation)
  • Reasoning with word vectors (Word2vec)
  • Getting words in order with convolutional neural networks (CNNs)
  • Loopy (recurrent) neural networks (RNNs)
  • Improving retention with long short-term memory networks
  • Sequence-to-sequence models and attention
• PART 3 – GETTING REAL (REAL-WORLD NLP CHALLENGES)
  • Information extraction (named entity extraction and question answering)
  • Getting chatty (dialog engines)
  • Scaling up (optimization, parallelization, and batch processing)

Why this Book is Good?

This book binds many of the NLP methods/tools together into a feasible application package. This book has great coverage of NLP methods.

Who Should read this Book?

• No doubt this book is great for Beginners in NLP, but it requires a basic understanding of deep learning and intermediate Python skills.

Where to find this Book?

• You can buy this book on Amazon- Natural Language Processing in Action

So, this is my List on Best Books for Natural Language Processing. Now its time to wrap up.

Conclusion

In this article, you discovered the 10 Best Books for Natural Language Processing. Have you Bought or Read anyone of these Books?. If yes then tell your experience in the comment section.

I hope these 10 Best Books for Natural Language Processing will help you to learn Natural Language Processing Concepts.

Enjoy Learning!

All the Best!

Explore more about Artificial Intelligence.

Thank YOU!

Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford