Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems Paperback – 30 June 2020
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About the Author
How Practical NLP was born
The authors have been building and scaling NLP solutions for over a decade at leading universities and technology companies. While mentoring colleagues and other engineers, they noticed a gap between NLP practice in the industry and the NLP skill set of new engineers or those who are experienced but just starting with NLP in particular. Authors started understanding these gaps even better with NLP workshops they were conducting for industry professionals where they noticed that business and engineering leaders also suffer from these gaps.
Most of the online courses and books tackle NLP problems using toy use cases and popular (often large, clean and well defined) datasets. While this teaches the readers general methods, authors have seen that it does not give enough foundation to tackle new problems and develop complete solutions in the real world. Commonly encountered problems while building real world applications such as data collection, working with noisy data and signals, incremental development of solutions, and issues involved in deploying the solutions as a part of a larger application are generally not dealt with by existing resources on the topic. They also saw best practices to develop NLP systems were missing in most scenarios and this book was needed to bridge this gap.
What the book covers
This book gives a comprehensive view on building real world NLP applications. it covers the complete lifecycle of a typical NLP project - right from data collection to deploying and monitoring the model. Some of these steps are applicable to any ML pipeline while some are very specific to NLP. The book also introduces task-specific case studies and domain-specific guides to build an NLP system from scratch. Specifically it covers a gamut tasks ranging from text classification to question answering, information extraction to dialog systems. Similarly, it provides recipes to apply these tasks in domains ranging from e-commerce to healthcare, social media to law. The book also covers case studies and best practices from the viewpoint of business, engineering and product leaders to help them run NLP projects smoothly.
Owing to the depth and breadth of the topics and scenarios that are covered, the book does not go step by step explaining the code and all the concepts. Please refer to the detailed source code notebooks for details of the implementation. The code snippets given in the book cover the core logic and often skip introductory steps like setting up a library or importing a package as they are covered in the associated notebooks. To cover the wide range of concepts the book provides more than 450 extensive references to delve deeper into these topics. This book will be a day-to-day cookbook giving you a pragmatic view while building any NLP system as well as be a stepping stone to broaden the application of NLP into your domain.
Please note that readers pursuing cutting-edge research in NLP may find some sections of the book rudimentary as it does not cover in-depth theoretical and technical details related to NLP concepts. Moreover, it is expected that the readers will follow the respective documentations for various frameworks used in the code examples.
This book is for:
- A software engineer or a data scientist who needs to build real-world NLP systems
- A machine learning engineer who has to iterate and scale NLP systems
- A product manager who needs to understand NLP and how it can be applied to their domain
- A business leader who wants to start a new venture based on NLP or incorporate the cutting edge of NLP in existing products
|Practical Natural Language Processing||Natural Language Processing with Python||Natural Language Processing with PyTorch||Natural Language Processing with Spark NLP|
|Natural Language Processing from O'Reilly Media||A Comprehensive Guide to Building Real-World NLP Systems||Analyzing Text with the Natural Language Toolkit||Build Intelligent Language Applications Using Deep Learning||Learning to Understand Text at Scale|
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Most helpful customer reviews on Amazon.com
I liked that this book extensively covered important and practical domains such as e-commerce and healthcare, which I don’t even remember seeing in currently available books. Especially I appreciated their “pipeline” approach to solving a problem with informative diagrams. And finally, I really enjoyed the plentiful references in the book that cover both depth and breadth.
I am going for a five star as the book stands to what it promised! Only con: I wish they could fix the formatting of the references in the kindle version.
What really stood out to me is how well this book has been laid out coaxing a beginner through various aspects of language and then leading them through the complex technical aspects with tremendous lucidity. The code samples serve to further reinforce these concepts as the authors seem to understand that the best way to really learn something is by doing. In my experience reading articles and books on ML/NLP, there are a lot of assumptions about the reader. I found it refreshing that this book makes no such assumptions. How many books do you know that actually explain what an "embedding" is? That being said, one of the major strengths of this book is that it effortlessly combines theoretical aspects with practical advice on building real world NLP systems. It is evident that the authors have first hand experience in building scalable machine learning systems and have thoroughly addressed many of these challenges in the book.
The one issue I did find while reading this book is that the images were not sharp enough and I had sometimes squint to read what was written. All-in-all I think this is book does a terrific job at breaking down complex technical aspects of NLP and is also a very handy reference book for experts to jog their memory about key concepts. I also found it extremely useful for interviews since a lot of the concepts are laid out in a very easy to understand fashion.