Natural Language Processing (NLP) is an essential component of artificial intelligence that refers to the computer program that can understand human language. NLP approaches are used in businesses to develop smart assistants, email filters, predictive texts, language translations, digital phone calls, data analysis, and text analysis. Due to such developments, business operations are seamlessly performed with NLP. Therefore, there is a requirement for NLP professionals who can simplify business processes. So if you are looking for a reference for NLP, this article will guide you to top ten mainly used NLP books that are readily available on Amazon.
Natural language processing books
Below is the list of the widely read natural language processing books on Amazon.
- Practical Natural Language Processing
The Practical Natural Language Processing book is written by Sowmya Vajjala, Anju Gupta, Bodhisattwa Majumder, and Harshit Surana. This is the best book if you want to build, iterate, and scale NLP systems in your business.
The authors of this book guide you through the entire process of building real-world NLP solutions. This book allows you to learn to adapt NLP solutions for social media, healthcare, and retail industries. It also helps you understand NLP’s wide range of problem statements, tasks, and solution approaches.
After reading this book, you can implement and evaluate different NLP applications using deep learning and machine learning methods and modify your NLP solutions based on your business problems. You can also evaluate various algorithms and approaches for NLP product tasks, datasets, and more.
Link to the book: Practical Natural language Processing
- Natural Language Processing with Python
Written by Steven Bird, Edward Loper, and Ewan Klein, Natural Language Processing with Python book introduces natural language processing with Python language. It will teach you to write Python scripts that work with large unstructured text data.
This book contains examples and exercises that help you extract information from unstructured text and identify named entities. With this book, you can also learn to analyze the texts’ linguistic structure and semantic analysis.
This book allows readers to use popular linguistic datasets like treebanks and WordNet. It guides you in gaining practical skills in NLP and the Natural Language Processing Toolkit (NLTK) open-source library. If you want to develop web applications, analyze multilingual news sources, and are curious about knowing how human language works, you can find this book very useful and exciting.
Link to the book: Natural Language Processing with Python
- Text Mining with R
Released in June 2017, Text Mining in R book by Julia Silge and David Robinson allows you to explore text mining techniques with tidytext. Authors have developed the tidytext package to use the tidy principles behind R packages like ggraph and dplyr. This book will teach you how tidytext and other tidy tools in R make text analysis easier.
The authors of this book teach how treating text as data frames allow you to manipulate, summarize and visualize characteristics of texts. It also guides you in integrating natural language processing into effective workflows. It contains practical code examples and data explorations that help you gain real insights from news, literature, and social media.
By reading this book, you can learn to apply the tidytext package to NLP, use sentiment analysis to detect the emotional content of texts, identify essential terms in documents with frequency measurements, use topic modeling to classify document collections into natural groups, and explore relationships between ggraph and dplyr packages.
Link to the book: Text Mining with R
- Introduction to Natural Language Processing
Introduction to Natural Language Processing by Jacob Eisenstein offers a technical perspective on natural language processing. This book teaches you methods that understand, manipulate and generate human language. It highlights the contemporary data-driven approaches that focus on techniques from supervised and unsupervised machine learning techniques.
The book’s first section teaches you the foundations of machine learning by building a set of tools used for word-based textual analysis. The second section focuses on structured representations of language that include sequences, trees, and graphs. The third section refers to the different approaches for the representation and analysis of linguistic meaning that range from formal logic to neural word embeddings. Lastly, the book’s final section provides chapter-length treatments of three transformative applications of natural language processing information extraction, text generation, and machine translation.
Link to the book: Introduction to Natural Language Processing
- Deep Learning in Natural Language Processing
By Li Deng and Yang Liu, Deep Learning in Natural Language Processing describes the art of deep learning research and its applications to NLP tasks like speech recognition, lexical analysis, knowledge graphs, sentiment analysis, social computing, and parsing.
This book covers all the essential tasks and techniques of natural language processing. It consists of an up-to-date and comprehensive survey of deep learning research and its application in natural language processing.
Any undergraduate, postgraduate or post-doctoral researchers, industrial researchers, lecturers, and someone interested in learning deep learning with natural language processing can read this book.
Link to the book: Deep Learning in Natural Language Processing
- Neural Network Methods in Natural Language Processing
The Neural Network Methods in Natural Language Processing book by Yoav Goldberg and Graeme Hirst highlights the applications of neural network models to natural language data. The book’s initial section covers the basics of supervised machine learning and feed-forward neural networks. It also covers the basics of machine learning and using vector-based instead of symbolic representation for words.
The book also guides you on the computation-graph abstraction that allows you to easily define and train arbitrary neural networks. It also introduces you to the more specialized neural network architecture that consists of 1D convolutional neural networks, conditioned generation models, recurrent neural networks, and attention-based models. These neural network architectures are the backbone behind the algorithms for syntactic parsing, machine translation, and many other applications.
Other essential concepts like tree-shaped neural networks, prospects of multi-task learning, and structured prediction are also explained in this book.
Link to the book: Neural Networks Methods in Natural Language Processing
- Taming Text
The Taming Text on Natural language processing by Grant S. Ingersoll, Thomas S. Morton, and Drew Farris is the complete guide to work on unstructured texts in real-world examples. It teaches you to organize texts using approaches like proper name recognition, full-text searches, clustering, tagging, summarization, and information extraction. This book explains all the concepts with examples.
You do not need prior statistics or natural language processing knowledge while reading the Taming Text textbook. The examples in this book are in Java language, but you can apply the concepts in any programming language. The book provides an understanding of open-source libraries like Solr and Mahout. It also teaches you to build text-processing applications.
Link to the book: Taming Text
- Natural Language Processing with Java
The Natural Language Processing with Java book by Richard M Reese and Ashish Singh Bhatia teaches various approaches for organizing and extracting useful texts from unstructured data using Java. This book guides on popular Java libraries like CoreNLP, OpenNLP, and Mallet.
In this book, you will learn the basics of NLP and how it helps to identify patterns, company names, unique names, and more in sentences. It also teaches you to perform language analysis with the help of Java libraries to gain insights from the sentences. After reading the book, you can perform tasks such as tokenizations, parts of speech, model training, and parsing trees on sentences.
This book also explains statistical machine translation, dialog systems, summarization, complex searches, and supervised unsupervised NLPs.
Link to the book: Natural Language Processing with Java
- Natural Language Processing in Action
Written by Hobson Lane, Hannes Hapke, and Cole Howard, Natural Language Processing in Action guides you in creating machines that understand human language with Python programming language. This book explains traditional NLP approaches, including neural networks, modern deep learning algorithms, and generative techniques that can tackle real-life problems like composing texts, extracting dates, and answering free-form questions.
Reading this book requires a basic understanding of deep learning and intermediate Python skills. With this book, you will learn to work with Python libraries like Keras, TensorFlow, scikit-learn, and gensim. You will also learn about the rule-based, data-based NLPs and their practices.
Link to the book: Natural Language Processing with Action
- Handbook of Natural Language Processing
Written by Nitin Indurkhya and Fred J. Damerau, the Handbook of Natural Language Processing highlights tools and techniques for implementing NLP in computer systems. This book is divided into three sections: the first section consists of surveys of classical techniques that include both symbolic and empirical approaches. The section highlights the statistical approaches to natural language processing. In the final section, every chapter describes the specific application class, from Chinese machine translation to information visualizations, biomedical text mining, and more.
This book focuses on practically implementing natural language processing with tools. It consists of chapters like text preprocessing, lexical analysis, semantic analysis, syntactic analysis, and more.
Link to the book: Handbook of Natural Language Processing
