The internet is full of resources for learning new programming skills. For booming industries such asNatural Language Processing (NLP)You can find many tutorials, video series and university lectures online. All of these formats are great ways to get started. However, if you want to really dig into a new topic, there's nothing like a good book. In this post, we've compiled a list of our favorite books to keep in your backpack before you start your NLP journey.
why you should read
research has shownWhen students learn about a process from printed books rather than online, they can better understand the details of the process. While reading a book usually takes a lot longer than watching a YouTube tutorial, the time you spend pays off: you spend more time thinking about the topic and ultimately gain a deeper understanding.
While hard copies of scientific books can be expensive, some of the NLP books on this list are available for free. Thanks to the open-source spirit of the programming community, many authors offer paid and free versions of their books, so you can read carefully written content from some of NLP's greatest minds.
What is Natural Language Processing?
NLP describes a field of computer science whose goal is to make human language available for machine processing. A closely related term isComputational Linguistics(CL)。Although there are subtle differences between the two (CL is more theory and research, while NLP is more practical), they are often used interchangeably.
If you want to learn more about NLP and why it's such a hot topic in AI, read our blog postWhat is Natural Language Processing?。
The Six Best NLP Books
Natural Language Processing with Python
Das Natural Language Toolkit(NLTK) is the dinosaur of Python NLP libraries. It's relatively old and a bit clunky - but still popular with NLP practitioners, who appreciate its comprehensiveness and powerful nature.Natural Language Processing with PythonThis is NLTKstudy guide, by authors Steven Bird, Ivan Klein, and Edward Loper. Often referred to as the "NLTK book", it focuses almost entirely on practical examples and aims to teach you how to use the library while introducing you to the core concepts of NLP.
Due to the lack of theory and mathematical formulas, this book is very suitable for beginners. In fact, one of the goals of this book is to teach Python programming through an NLP perspective. What we really appreciate about this book is the author's focus on dealing with data bodies - an aspect that is often forgotten in more theoretical books, but is an important part of working as an NLP practitioner.
Language and Language Processing
Classical is the heavier side of theoryLanguage and Language ProcessingAuthors: Dan Juravsky and James H. Martin. Despite its unassuming title, this book covers every NLP topic you could possibly want and more. The authors are not afraid to use linguistic concepts such as dependency analysis and constituency grammar, and also cover topics popular in industrial settings, such as chatbots and machine translation.
The electronic version of this book has been updated to reflect the latest scientific developments. It includes several chapters on neural networks and an advanced module on question answering systems. Each chapter is a self-contained unit with references, exercises, or historical notes. The authors expect readers to have a basic knowledge of linguistics, mathematics, and computer science concepts.
Statistical Methods for Speech Recognition
Most NLP publications are implicitly oriented towards processing written speech, but processing spoken speech brings its own challenges.Statistical Methods for Speech Recognition Frederick Jelinek has specialized in the problem of using computational methods for speech recognition - a fundamental problem in the ever-expanding field of speech-based applications.
The chapters build on each other, with the first part of the book dealing with statistical and methodological fundamentals. Subsequently, increasingly sophisticated speech recognition applications are being developed. The density of the book makes for an informative read, but it does require some basic knowledge of mathematics, especially probability theory.
Handbook of Computational Linguistics and Natural Language Processing
Handbook of Computational Linguistics and Natural Language Processing, edited by Alexander Clark, Chris Fox, and Shalom Lappin, is a carefully edited collection of papers by experts in various fields of NLP. The volume begins with an introduction to formal languages and ends with a discussion of complex NLP applications such as question answering, natural language generation, and discourse analysis.
Meanwhile, Martha Palmer and Xue Nianwen discuss the practical aspects of computational linguistics in their chapter on annotated corpora.
Although the book is a bit old (published in 2010), its collection of different topics and authors makes it a valuable source of information and a good starting point for understanding broader NLP-related topics.
Linguistic Foundations of Natural Language Processing
While some NLP applications have processing pipelines that accept raw text as input, others expect some level of preprocessing. Text preprocessing requires at least some knowledge of linguistic concepts and their application in NLP.
For those interested in NLP but without any language skills:Linguistic Foundations of Natural Language ProcessingBy Emily M. Bender To the rescue. All the basic concepts of grammar and morphology are introduced, with a focus on how these concepts can help improve NLP applications.
the band is part of itComposition Lectures on Human TechnologyMorgan and Claypool Collection. This series contains posts on many other interesting topics, such as automatic error detection and speech refinement.
Neural Network Approaches to Natural Language Processing
No list of NLP books is complete without deep learning resources. This computationally intensive, mathematically complex discipline has given us some of the most impressive NLP applications in recent years, from translation systems to automatic captioning.
Joseph Goldberg, authorNeural Network Approaches to Natural Language Processingis a professor at Bar-Ilan University in Israel and has published numerous scientific papers on neural network NLP. He makes sure to cover fundamentals such as supervised learning, deep learning, and the challenges of processing linguistic data before moving on to increasingly complex neural architectures.
Alternative resources for learning NLP
Arguably one of the great benefits of studying computer science is the accessibility of most materials. If you want to learn more about the latest advances in NLP, just visitarxiv, source of academic papers.
Researchers from a variety of disciplines upload their own work to this online archive, many of which were written recently, without even going through the peer-review process required for publication in a scientific journal.
Reading complex scientific papers can seem daunting at first, but it's a great way to keep up with developments in a field as fast-moving as NLP, and it turns out that most papers aren't too long.
summarize
In this article, we review some of the most important natural language processing books. We discuss why it's good to incorporate reading into your self-study program and show you how to find the latest research on topics that interest you.
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