16 Recommended books on natural language processing!

6 Recommended Books for Natural Language Processing for Beginners

Here are 6 recommended books for beginners.

1. Fundamentals of natural language processing
2. Fundamentals and techniques of natural language processing
3. Introduction to natural language processing using machine learning and deep learning
4. An Introduction to Machine Learning for Language Processing
5. Natural Language Processing [Revised Edition]
6. Introduction to Natural Language Processing with BERT

I will explain each.

( 1 ) Fundamentals of natural language processing 

• Content

This book is a textbook for undergraduate students on natural language processing, which is a technology for processing human-readable and written language (natural language) on a computer.

Each analysis step in natural language processing is explained in an easy-to-understand manner that even beginners can understand, so this book can be called an introductory book on natural language processing.

• Reader reviews

The technology related to natural language processing was comprehensively and carefully summarized. I think it’s a must-read for anyone who wants to get into natural language processing but doesn’t know how to get started.

Read this book, understand the basics of natural language processing, think about what you want to do with natural language processing, and use the tools and books that meet your goals.

( 2 ) Fundamentals and techniques of natural language processing

• Content

With the advancement and speeding up of hardware and software, the technology called ” Natural Language Processing ” is reaching the level of practical use.

This book is a compact illustration of the technical and business basic knowledge of this natural language processing.

• Reader reviews

There are no formulas in this book. We use familiar examples such as search engines to give an overview of what can be achieved with natural language processing, to what extent, and where there are difficulties. Only basic ideas are given for the logic used there.

Can I do something interesting with natural language processing? I wanted to find out, so this book was very suitable. If you’re a similar person, I think it’s better to first get an overview in this book and then learn the details in other books.

③Introduction to natural language processing using machine learning and deep learning

• Content

This book explains natural language processing from the basics so that even people who have never studied it before can learn it.

In order to process natural language on a computer, what kind of processing is done in advance, how words and sentences are analyzed, and what kind of processing is done to execute tasks such as automatic translation , etc. will be briefly explained.

A feature of this book is that most implementations use Japanese data. If you try to apply a natural language processing method using machine learning to Japanese, you will immediately hit the wall of the dataset. In light of this situation, this book allows you to try various tasks of natural language processing on Japanese datasets.

• Reader reviews

Explains natural language processing from the basics along with sample code. All the code can be executed on Google Colab, so I’m grateful that I don’t have to waste time building the environment.

I think that there were parts where the explanation required a little prerequisite knowledge, but I felt that it was mostly within the scope of researching for the first time.

➃Introduction to machine learning for language processing

• Content

This book aims to convey the basic ideas to understand the use of machine learning in natural language processing.

The essential knowledge carefully selected from the vast field of this field is described, and it can be said that it is a book that you should definitely read before picking up a paper or commentary.

• Reader reviews

I think that it is good to read after the textbook ” Natural Language Processing ” of the Open University of Japan. It covers a wide range of topics related to language processing, and the models are well explained.

Compared to the textbooks of the Open University of Japan, there are more formulas, but if you read slowly, you will find that they are not that difficult.

After reading this book, you can proceed to specialized books/papers as needed while implementing.

➄Natural Language Processing [revised edition]

• Content

With the recent development of computers and computer networks, natural language processing technology has advanced dramatically, and application systems such as web searches, dialogue systems, and machine translation have begun to permeate our daily lives.

In this book, we will explain the properties of natural language behind it, the algorithms and mechanisms for processing natural language by computer, the difficulty, future developments, etc.

• Reader reviews

I think it’s a great book for understanding the basics. Other books lacked a systematic explanation of basic terms and concepts, and I had a hard time understanding them.

It’s a thin book with no concrete examples of coding, but you can rediscover the joy of reading and studying textbooks.

⑥ Introduction to Natural Language Processing with BERT

• Content

This book is an introduction to BERT, which has played a major role in the recent development of natural language processing and is also useful in applications. After an overview of natural language processing and machine learning in the first half, we will solve various tasks with BERT in the second half.

Specifically, we handle sentence classification, named entity extraction, grammar proofreading, similar sentence search, and data visualization. The goal is to be able to use BERT by yourself by experiencing a series of processes from data set processing, fine tuning (learning to specialize BERT for a specific language task), and performance evaluation. is.

• Reader reviews

As it says “Introduction to Natural Language Processing”, it explains the implementation of deep learning models that deal with natural language related problems.

For those who want to learn more about the mechanism of natural language models such as RNN and Transformers, I recommend reading Deep Learning from Scratch Volume 2 (Natural Language Processing) before starting this book.

It introduces various techniques for working on specific tasks in Japanese, which is generally considered to be difficult to handle, and I thought it was a good book unlike any other. If you are interested in Japanese natural language models, please read it!

5 Recommended Books for Intermediate Natural Language Processing

The following five books are recommended for intermediate-level students who want to learn the basics of natural language processing and want to learn more advanced natural language processing.

1. Natural language processing by deep learning
2. Technology supporting Japanese input
3. Speech and Language Processing – Speech and Language Processing
4. Natural Language Understanding – Understanding Natural Language
5. PyTorch Natural Language Processing Programming

I will explain each.

(1) Natural language processing by deep learning

• Content

This book explains how to use deep learning, focusing on applications of natural language processing (machine translation, document summarization, dialogue, question answering). Practical content that can only be read in this book, such as ingenuity in implementation, is also substantial.

• Reader reviews

The level of skill required of the intended reader is relatively high. It is ideal for those who have some understanding of the basic mechanisms of neural networks and want to find out how deep learning can be used in the field of natural language processing.

Basic knowledge of natural language processing is not necessarily required, but basic terms and concepts are common even before deep learning, so a simple study will help you understand.

Note that although there are some implementation and optimization topics, it is primarily an academic book, not a “write and learn” type of book.

( 2 ) Technology supporting Japanese input

• Content

This book introduces Japanese input, especially kana-kanji conversion, and how to make it, using sample code.

This book is written not only for those who are interested in Japanese input, but also for those who are interested in natural language processing and machine learning and want to touch on specific applications once.

• Reader reviews

It has a lot of pseudo code and is easy to understand. Also, since there are many explanations of algorithms, you can learn the mechanism of Japanese input in depth.

A book that should be read not only by those who study Japanese input, but also by those who study natural language processing.

③ Speech and Language Processing

• Content

This book covers natural language processing , its history and speech recognition technology. Therefore, it is a book for those who want to learn about the relationship between machine learning and natural language.

However, since it is a specialized book with no translated version, it can be said that it is a book with a high degree of difficulty in reading.

➃ Natural Language Understanding

• Content

This book provides a well-balanced explanation of syntactic analysis, semantic analysis, and contextual analysis in natural language processing.

There is no translated version here either, but it is characterized by not using a lot of technical terms.

Also, this book can be said to be a book for people who want to learn natural language processing in English, just like “Speech and Language Processing”.

➄ PyTorch Natural Language Processing Programming

• Content

This book explains how to implement programs using deep learning technology more easily by using the machine learning framework PyTorch.

The purpose of the program created in this book is to “classify the parts of speech of words in a sentence”, “perform Japanese-to-English machine translation”, and “return answers to questions”.

• Reader reviews

I was able to understand the basics somehow by following the book along with the sample program.

It’s my first time using PyTorch, so I don’t know how much it can do in the field of natural language analysis, but I think it would have been nice to have a more practical and applied sample program.

5 recommended books for learning natural language processing by programming language

The following five books can be learned in Python, Java , C++, and C, which are programming languages ​​that can perform natural language processing .

1. Learn by moving with Python Introduction to natural language processing
2. Python Natural Language Processing 101 Knocks ~From Basics to BERT~
3. Learning Natural Language Processing and Machine Learning in Java
4. Deep Learning with C++
5. Natural language processing and deep learning

I will introduce each of them.

① Learn by running with Python Introduction to natural language processing

• Content

This book is for readers who have programming experience in Python to experience natural language processing by creating and running web applications that perform natural language processing using various open source software (OSS) and libraries. .

In addition, you can learn various concepts and methods related to natural language processing, as well as simple theories, making it an ideal preparatory step for full-scale learning.

• Reader reviews

Since it covers a wide range of topics, from morphological analysis to the use of knowledge resources, it is not an introduction to natural language processing itself, but an introduction to handling natural language processing in Python .

I thought it was very easy to understand because the contents are simple and difficult calculation formulas and theories are omitted, and you can check from input to output with concrete code.

② Python Natural Language Processing 101 Knocks ~From Basics to BERT~

• Content

By actually writing a program in Python and practicing 101 questions, this book cultivates the ability to create software from scratch that produces amazing results in ” Natural Language Processing ” x “Deep Learning”. The purpose is that.

By practicing this book and doing it in order, we aim to be able to create software by ourselves using the technology of “neural machine translation”, which is also used in the famous “Google Translate”.

• Reader reviews

Compared to image processing and machine learning with table data, I have the impression that there are few books that systematically organize information on natural language processing.

Among them, “Python natural language processing 101 knocks” describes from basics to remember to relatively new processing of BERT.

▼Quoted
from Amazon: Python Natural Language Processing 101 Knock ~From Basics to BERT~

③ Learn natural language processing and machine learning with Java

• Content

This book provides easy-to-understand explanations of the use of machine learning in natural language processing, with many Java program examples.

Not only does it carefully explain the basics of natural language processing and machine learning, but it also contains practice problems for expanding sample programs and a wealth of implementation examples, so you can learn theory and practice in a well-balanced manner. .

➃ Deep learning with C++

• Content

This book is intended for beginners and software engineers who are learning the basics of deep learning. It contains working C++ source code, from neural networks that are the basis of deep learning to derived technologies and applications . It is a content to learn while referring.

The first half deals with essential knowledge about neural networks, and the second half refers to neural network derivative technologies and applications, gradually advancing to advanced content.

• Reader reviews

If you just use the library, the contents remain a black box, but I think that trying to open the inside of the black box like this book is very useful for people who just use the library.

▼Quoted
from Amazon: Deep Learning with C++

➄Natural language processing and deep learning

• Content

This book provides an easy-to- understand explanation of ” Natural Language Processing ,” which is one of the main pillars of artificial intelligence research . It explains in an easy-to-understand manner the theory of machine learning, which is often used in the field of natural language processing in artificial intelligence research, and teaches what deep learning is based on that knowledge.

Instead of simply listing concepts, we will introduce a wide variety of implementation examples and practice problems so that you can learn the theory and practice of natural language processing in a well-balanced manner.

• Reader reviews

An introductory level book on natural language processing with machine learning. Although the title is exaggerated, the content is limited to explanations of the basic concepts of machine learning and natural language processing and the implementation of the simplest examples.

Specifically, only the simplest examples of n-grams and bag-of-words in the field of natural language processing, and CNN and RNN in the field of machine learning are carefully explained using diagrams and source code examples.

If you’re comfortable with the C language, I think it’s very suitable as the first introductory book for people who don’t know anything about implementing machine learning.

This time, we have introduced “recommended books for learning natural language processing” by learning level and programming language.

Natural language processing is now an indispensable technology in our lives, as it is used for machine translation such as Google Translate and DeepL, and even for character conversion prediction functions.

And since it is a technology that is expected to evolve further in the future, if you are wondering which book to buy, please refer to the books introduced in this article for learning and development.