Tweet Share Share

Last Updated on August 14, 2020

Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data.

The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models.

In this post, you will discover the top books that you can read to get started with natural language processing.

After reading this post, you will know:

The top books for practical natural language processing.

The top textbooks for the theoretical foundations of natural language processing.

The NLP books I have on my shelf.

Kick-start your project with my new book Deep Learning for Natural Language Processing, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

Top Practical Books on Natural Language Processing

As practitioners, we do not always have to grab for a textbook when getting started on a new topic.

Code examples in the book are in the Python programming language.

Although there are fewer practical books on NLP than textbooks, I have tried to pick the top 3 books that will help you get started and bring NLP method to your machine learning project.

Written by Steven Bird, Ewan Klein and Edward Loper.

This book provides an introduction to NLP using the Python stack for practitioners.

The book focuses on using the NLTK Python library, which is very popular for common NLP tasks.

Contents include:

Language Processing and Python Accessing Text Corpora and Lexical Resources Processing Raw Text Writing Structured Programs Categorizing and Tagging Words Learning to Classify Text Extracting Information from Text Analyzing Sentence Structure Building Feature-Based GRammars Analyzing the Meaning of Sentences Managing Linguistic Data

This book is perfect if you are looking at getting into classical NLP using the go-to NLTK platform.

Resources

This book provides an introduction to a suite of different NLP tools and problems, such as Apache Solr, Apache OpenNLP, and Apache Mahout.

Code examples are in Java.

It may be more suited to developers getting started with larger enterprise-grade NLP tools on work projects.

Written by Grant Ingersoll, Thomas Morton and Drew Farris.

Notably, Grant Ingersoll is a cofounder of the Apache Mahout project.

Contents include:

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 Untaming Text: Exploring the Next Frontier

Resources

Need help with Deep Learning for Text Data? Take my free 7-day email crash course now (with code). Click to sign-up and also get a free PDF Ebook version of the course. Start Your FREE Crash-Course Now

Written by Julia Silge and David Robinson.

This book demonstrates statistical natural language processing methods on a range of modern applications.

Code examples are in R.

Code focuses on the “tidy” principles by Hadley Wickham (paper) and the tidytext package by the authors.

Of the three books, this is the most recently published and has a more practical and modern feel to the demonstrations.

Contents include:

The Tidy Text Format Sentiment 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 Case Study: Comparing Twitter Archives Case Study: Mining NASA Metadata Case Study: Analyzing Usenet Text

Resources

Do you know of other great practical books on natural language processing?

Let me know in the comments.

Top Textbooks on Natural Language Processing

There are a ton of textbooks on natural language processing and on specific sub-topics.

In this section, I have tried to focus on what I (and consensus) seems to see as the best books on the topic for beginners, e.g. undergraduate or graduate students and practitioners looking to step deeper into the theory.

I have tried to pick a mix of general NLP books as well as books on highly studied topics like translation and speech.

The first two books in this section are essentially cannon for NLP students.

Written by Christopher Manning and Hinrich Schütze.

Notably, Christopher Manning teaches NLP at Stanford and is behind the CS224n: Natural Language Processing with Deep Learning course.

This book provides an introduction to statistical methods for natural language processing covering both the required linguistics and the newer (at the time, circa 1999) statistical methods.

This book provides a strong foundation to better grasp the newer methods and encodings.

Contents include:

Introduction Mathematical Foundations Linguistic Essentials Corpus-Based Work Collocations Statistical Inference: n-gram Models over Sparse Data Word Sense Disambiguation Lexical Acquisition Markov Models Part-of-Speech Tagging Probabilistic Context Free Grammars Probabilistic Parsing Statistical Alignment and Machine Translation Clustering Topics in Information Retrieval Text Categorization

Resources

Written by Daniel Jurafsky and James Martin.

This book provides coverage of NLP from both speech and text perspectives with a strong focus on applications (one in each chapter).

Coverage of the topic feels exhaustive.

Contents include:

Introduction Regular Expressions and Automata Words and Transducers N-grams Part-of-Speech Tagging Hidden Markov and Maximum Entropy Models Phonetics Speech Synthesis Automatic Speech Recognition Speech Recognition: Advanced Topics Computational Phonology Formal Grammars of English Syntactic Parsing Statistical Parsing Features and Unification Language and Complexity The Representation of Meaning Computational Semantics Lexical Semantics Computational Lexical Semantics Computational Discourse Information Extraction Question Answering and Summarization Dialog and Conversational Agents Machine Translation

Resources

Written by Philipp Koehn.

This book provides an introduction to the topic of statistical machine translation, a s subfield of NLP.

Contents include:

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

Resources

Written by Frederick Jelinek.

This book provides an introduction to the topic of statistical speech recognition, another subfield of NLP that saw an overhaul in the 1990s with statistical approaches.

Contents Include

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

Resources

NLP Books that I Own

I like to have a mixture of practical and reference texts on my shelf.

The hard part of NLP (for me) is simply the large number of sub-problems and the specialized terminology and theory used.

For this reason I have the following 3 NLP textbooks on my shelf:

I also really like the look of:

I recommend choosing the NLP books that are right for you and your needs or project.

Let me know which books you chose or own.

Leave a comment below.

Further Reading

This section provides more resources on the topic if you are looking go deeper.

Top NLP Books

Quora

Summary

In this post, you discovered the top books on natural language processing.

Specifically, you learned:

The top books for practical natural language processing.

The top textbooks for the theoretical foundations of natural language processing

The NLP books I have on my shelf.

Do you have any questions?

Ask your questions in the comments below and I will do my best to answer.

Develop Deep Learning models for Text Data Today! Develop Your Own Text models in Minutes ...with just a few lines of python code Discover how in my new Ebook:

Deep Learning for Natural Language Processing It provides self-study tutorials on topics like:

Bag-of-Words, Word Embedding, Language Models, Caption Generation, Text Translation and much more... Finally Bring Deep Learning to your Natural Language Processing Projects Skip the Academics. Just Results. See What's Inside