Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems: Vajjala, Sowmya, Majumder, Bodhisattwa, Gupta, Anuj, Surana, Harshit: 9781492054054: Amazon.com: Books

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Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.

Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You'll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail.

With this book, you'll:

Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP
Implement and evaluate different NLP applications using machine learning and deep learning methods
Fine-tune your NLP solution based on your business problem and industry vertical
Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages
Produce software solutions following best practices around release, deployment, and DevOps for NLP systems
Understand best practices, opportunities, and the roadmap for NLP from a business and product leader's perspective

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Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

From the Publisher

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

Customer Reviews	218	217	63	18
Price	$71.92	$41.05	$63.99	$36.16
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

Editorial Reviews

Review

This book is ideal both as a first resource to discover the field of natural language processing and a guide for seasoned practitioners looking to discover the latest developments in this exciting area.

- Julian McAuley, Professor, UC San Diego

Practical NLP focuses squarely on an overlooked demographic: the practitioners and business leaders in industry!

- Zachary Lipton, Scientist at Amazon AI, Author of Dive into Deep Learning, Professor, Carnegie Mellon University

This book does a great job bridging the gap between natural language processing research and practical applications.

- Sebastian Ruder Scientist, Google DeepMind, Author of newsletter NLP News

This book offers the best of both worlds: textbooks and 'cookbooks'. If you would like to go from zero to one in NLP, this book is for you!

- Marc Najork, Director, Google AI, ACM & IEEE Fellow

This book is a must for all aspiring NLP engineers, entrepreneurs who want to build companies around language technologies.

- Monojit Choudhury, Principal Researcher, Microsoft, Faculty at IIT Kharagpur

There is much hard-fought practical advice from the trenches. A must-read for engineers building NLP applications.

- Vinayak Hegde, CTO-in-Residence, Microsoft For Startups

I feel this is not only an essential book for NLP practitioners, it is also a valuable reference for the research community.

- Mengting Wan, Data Scientist at Airbnb, Microsoft Research Fellow

The authors achieved a rare feat by simplifying the esoteric art of design and architecture of production quality ML systems.

- Siddharth Sharma, ML Engineer, Facebook

This book gives a consolidated look at modern practice, starting from an MVP and building up to examples for sophisticated use cases.

- Ed Harris, CEO and co-founder at SharpestMinds (YC W18)

From the Author

We wrote the book 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

Please note that readers pursuing cutting-edge research in NLP may find some sections of the book rudimentary as we do not cover in-depth theoretical and technical details related to NLP concepts. Moreover, we expect the readers to follow the respective documentations for various frameworks we use in our code examples.

From the Inside Flap

THE PHILOSOPHY

We want to provide a holistic, yet, practical perspective which enables the reader to successfully build real world NLP solutions embedded in larger product setups. Thus, most chapters are accompanied by code walkthroughs in the associated git repository. The book is also supplemented with extensive references at the end of each chapter for the readers who want to delve deeper. Throughout the book, we start with a simple solution and incrementally build more complex solutions, by taking a Minimum Viable Product (MVP) approach, as commonly found in industry practice. We also give tips wherever possible based on our experience and learnings. Where possible, each chapter is accompanied by a discussion on the state of the art in that topic. Most chapters conclude with a case study taking real world use cases.

Consider the task of building a chatbot or text classification system at your organization. In the beginning there may be little or no data to work with. At this point a basic solution using rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated NLP techniques (which are often data intensive) can be used including deep learning. At each step of this journey there are dozens of alternative approaches one can take. This book will help you navigate this maze of options.

SCOPE
This book gives a comprehensive view on building real world NLP applications. We will cover 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. We also introduce task-specific case studies and domain-specific guides to build an NLP system from scratch. Specifically we cover a gamut tasks ranging from text classification to question answering, information extraction to dialog systems. Similarly, we provide recipes to apply these tasks in domains ranging from e-commerce to healthcare, social media to finance. Owing to the depth and breadth of the topics and scenarios we cover, we will not go step by step explaining the code and all the concepts. For details of the implementation, we have provided detailed source code notebooks. 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 we have given 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.

From the Back Cover

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.

About the Author

Sowmya Vajjala has a PhD in Computational Linguistics from University of Tubingen, Germany. She currently works as a research officer at National Research Council, Canada’s largest federal research and development organization. Her past work experience spans both academia as a faculty at Iowa State University, USA as well as industry at Microsoft Research and The Globe and Mail.

Bodhisattwa Majumder is a doctoral candidate in NLP and ML at UC San Diego. Earlier he studied at IIT Kharagpur where he graduated summa cum laude. Previously, he built large-scale NLP systems at Google AI Research and Microsoft Research, which went into products serving millions of users. Currently, he is also leading his university team in the Amazon Alexa Prize for 2019-2020.

Anuj Gupta has built NLP and ML systems at Fortune 100 companies as well as startups as a senior leader. He has incubated and led multiple ML teams in his career. He studied computer science at IIT Delhi and IIIT Hyderabad. He is currently Head of Machine Learning and Data Science at Vahan Inc. Above all, he is a father and husband.

Harshit Surana is founder at DeepFlux Inc. He has built and scaled ML systems at several Silicon Valley startups as a founder and an advisor. He studied computer science at Carnegie Mellon University where he worked with the MIT Media Lab on common sense AI. His research in NLP has received over 200 citations.

The authors have been working on NLP problems since 2006. They hail from Carnegie Mellon, UC San Diego, U of Tübingen, and the Indian Institutes of Technology. They have built and deployed NLP and ML systems in both academia and industry, including Fortune 100 companies, Silicon Valley startups, the MIT Media Lab, Microsoft Research and Google AI. They have also taught NLP courses at US universities as a faculty and published dozens of research papers in the field with hundreds of citations. The book distills the authors' collective wisdom for building and iterating NLP systems. The book is also advised and reviewed by researchers and scientists from Microsoft, Facebook, Spotify and Stanford University.

Product details

Publisher ‏ : ‎ O'Reilly Media
Publication date ‏ : ‎ July 21, 2020
Edition ‏ : ‎ 1st
Language ‏ : ‎ English
Print length ‏ : ‎ 454 pages
ISBN-10 ‏ : ‎ 1492054054
ISBN-13 ‏ : ‎ 978-1492054054
Item Weight ‏ : ‎ 2.31 pounds
Dimensions ‏ : ‎ 7 x 0.92 x 9.19 inches
Best Sellers Rank: #968,194 in Books (See Top 100 in Books)
- #236 in Data Mining (Books)
- #313 in Natural Language Processing (Books)
- #385 in Data Processing
Customer Reviews:
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Harshit Surana
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Harshit Surana is a cofounder at DeepFlux. He has built and scaled ML systems and engineering pipelines at several Silicon Valley startups as a founder and an advisor. He studied computer science at Carnegie Mellon University where he worked with the MIT Media Lab on common sense AI. His research in NLP has received over 200 citations.
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Anuj Gupta
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Sowmya Vajjala
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Bodhisattwa Majumder
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Bodhisattwa Majumder is a doctoral candidate in NLP and ML at UC San Diego. Earlier he studied at IIT Kharagpur where he graduated summa cum laude. Previously, he built large-scale NLP systems at Google AI Research and Microsoft Research, which went into products serving millions of users. Currently, he is also leading his university team in the Amazon Alexa Prize for 2019-2020.
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Matthew Emerick
Foundational to Learning NLP
Reviewed in the United States on March 9, 2021
Verified Purchase

About This Book
This was a highly regarded book when it came out in the summer of 2020, and I can see why. It's the generalist's guide to NLP, a high-level overview of the entire field. If you want to learn about natural language processing, this is where you start. There are books to read after this one, but this is your foundation.

Who Is This For?
The authors intent this book to be for anyone who is interested in or working in the field. However, they also have a list of prerequisites they want you to have to gain the maximum value from the book: an intermediate level of Python, knowledge of the general software development life cycle, a basic knowledge of machine learning, and some general knowledge of what NLP is. I can see where these can be useful, but I also agree with the authors that these are not completely necessary.

Why Was This Written?
The authors wrote this book to fill a perceived gap between what previous books offered versus what they think is required for working with natural language in production. From what I have seen from other books, I can agree.

Organization
The macrostructure of this book is well thought out and is broken up into four parts: foundations, essentials, applied, and bringing it all together. I really like these sections and how they include a map of reading order in the preface. This is useful when you already have a little bit of knowledge and want to see how best to skip around without missing anything.

Within the chapters, each one is different, but all end with a wrapping up section to summarize the chapter and give all the references. Having a summary at the end is great for helping the reader retain the information they just read. I never understood why some technical books leave this out.

Did This Book Succeed?
Yes, very much so. This is a foundational book for anyone learning natural language processing that paves the way for more detailed books when you want to drill down and specialize. It's well laid out and easy to navigate. This should be on the bookshelf of anyone working in artificial intelligence.

Rating and Final Thoughts
This book is step one to learning natural language processing and has earned all of the accolades it has been given. Buy this book and read through it to have a thorough understanding of the field without getting stuck in the details.

I give this book a 5 out of 5. This should be in your library.

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Shankar Saikia
Excellent introduction to NLP For Someone With Some AI/ML Knowledge
Reviewed in the United States on August 26, 2021
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I read quite a bit but it's not often that I read a book "front-to-back" (ie., the entire book). The books that I read "front-to-back" have something truly amazing about them. This book fits that category.

A little bit about myself: I have lived in Silicon Valley for over 30 years and I currently work as a Sales Engineer in the enterprise applications software space focusing on the supply chain/CRM sector. My background is 95% functional/business and 5% technical. I have read over 20 textbooks and/or technical books on AI/ML, including many of the ones recommended by AI authors (e.g., Intro to Machine Learning by Alpaydin, Deep Learning by Goodfellow/Bengio etc, Statistical Learning by Witten/Hastie etc, Hands-on Machine Learning with Scikit, Keras and Tensorflow by Geron, Reinforcement Learning by Sutton etc ... ). I love the ones mentioned in the parentheses AND yes I read almost 100% of most of them, often re-reading sections from all.
This book is one of those amazing books. It gave me a great education on many aspects of NLP, and a lot more depth than I had encountered anywhere else. I don't have enough time to write an exhaustive review of all the wonderful aspects about the book, but here is a short list of new things I learned and loved learning about ...
1. 4 aspects to a language (phonemes, morphemes and lexemes, syntax, context)
2. NLP tasks
3. NLP applications
4. NLP use cases in industry
My only complaint is that I wish the graphics were in color.
I recommend this book to anyone interested in learning about NLP ( ... as long as they have some background training in AI/ML concepts).
Excellent book ...

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calvinnme
This is not a bad book BUT...
Reviewed in the United States on January 29, 2021
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... the word "practical" in the title might make you think about the word "applied", as in code. That was what I was expecting. This is a great book to give a manager to read about NLP. They will understand things at a high level after reading it. However it just does not get the job done for someone like myself looking to apply this knowledge.

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Siddharth Narayanan
Excellent resource for building production level NLP pipelines!
Reviewed in the United States on December 22, 2020
Verified Purchase
First, a little bit about my background. I have been working in silicon valley for the past decade for a smattering of companies (both big and small). I am the typical software engineer who decided to switch to machine learning and have been building recommendation systems for the last 5 years. In fact, NLP is front and center in my current company (Hint: it is where you probably read most of your data science articles on the web) and this is where I found that the utility of the book shines through.

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.

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Abel
Quality is really bad
Reviewed in Germany on April 6, 2022
Verified Purchase

This is the first time I got a book which cover is correct, but the content is completely wrong.
How is it possible?
The book I got was "Infrastructure as Code", see Photo., but the cover says Practical Natural Language Processing.
Usually, I wouldn't give a bad review, but I read that many users complained about the book's quality. Well, I wouldn't suggest to buy the book until they fix their quality issues.

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Abel

Quality is really bad

Reviewed in Germany on April 6, 2022
This is the first time I got a book which cover is correct, but the content is completely wrong.
How is it possible?
The book I got was "Infrastructure as Code", see Photo., but the cover says Practical Natural Language Processing.
Usually, I wouldn't give a bad review, but I read that many users complained about the book's quality. Well, I wouldn't suggest to buy the book until they fix their quality issues.

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Amazon Customer
Folded Cover
Reviewed in Canada on October 26, 2022
Verified Purchase
Expensive book and well damaged on arrival

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Shreekant
Perfect
Reviewed in the United Kingdom on March 9, 2023
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Great content

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Ananda Swarup
A technical masterpiece, superb collection of topics
Reviewed in India on November 7, 2020
Verified Purchase
This is a book that was much needed in the current times. The book is essentially dividrd in the following order: a) text representation in a way comprehensive to a machine; b) the most common NLP tasks such as classification, entity recognition, knowledge dissemination; c) the above and beyond that is the tasks which involve cross engineering expertise such as social media mining etc.
The authors also offers some new gems like explicable AI where they show how-to explain the decision of a classifier; working with limited data. This book is definitely going to help a lot of practitioners and the book definitely covers a 360 degree view of the NLP spectrum.

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Ananda Swarup

A technical masterpiece, superb collection of topics

Reviewed in India on November 7, 2020
This is a book that was much needed in the current times. The book is essentially dividrd in the following order: a) text representation in a way comprehensive to a machine; b) the most common NLP tasks such as classification, entity recognition, knowledge dissemination; c) the above and beyond that is the tasks which involve cross engineering expertise such as social media mining etc.
The authors also offers some new gems like explicable AI where they show how-to explain the decision of a classifier; working with limited data. This book is definitely going to help a lot of practitioners and the book definitely covers a 360 degree view of the NLP spectrum.

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Chris
Qualité d'impression décevante
Reviewed in France on October 25, 2020
Verified Purchase
Très déçu de la qualité d'impression de ce livre vendu et expédié par Amazon. En effet, les images sont à peine visibles (voir photo 1) en comparaison à celles disponibles sur les extraits publiques du livre (voir photo 2). Cela sans parler de l'épaisseur de pages et de la sensation globale de n'avoir pas un produit O'Reilly sur la main. Retour et remboursement demandé.

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Chris

Qualité d'impression décevante

Reviewed in France on October 25, 2020
Très déçu de la qualité d'impression de ce livre vendu et expédié par Amazon. En effet, les images sont à peine visibles (voir photo 1) en comparaison à celles disponibles sur les extraits publiques du livre (voir photo 2). Cela sans parler de l'épaisseur de pages et de la sensation globale de n'avoir pas un produit O'Reilly sur la main. Retour et remboursement demandé.

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Top reviews from the United States

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5.0 out of 5 stars Foundational to Learning NLP

5.0 out of 5 stars Excellent introduction to NLP For Someone With Some AI/ML Knowledge

4.0 out of 5 stars This is not a bad book BUT...

5.0 out of 5 stars Excellent resource for building production level NLP pipelines!

Top reviews from other countries

1.0 out of 5 stars Quality is really bad

Quality is really bad

Images in this review

1.0 out of 5 stars Folded Cover

5.0 out of 5 stars Perfect

5.0 out of 5 stars A technical masterpiece, superb collection of topics

A technical masterpiece, superb collection of topics

Images in this review

1.0 out of 5 stars Qualité d'impression décevante

Qualité d'impression décevante

Images in this review

Foundational to Learning NLP

Excellent introduction to NLP For Someone With Some AI/ML Knowledge

This is not a bad book BUT...

Excellent resource for building production level NLP pipelines!

Quality is really bad

Folded Cover

Perfect

A technical masterpiece, superb collection of topics

Qualité d'impression décevante